The Influence of Academic Tracking on Adolescent Social Networks

ERIC Educational Resources Information Center

Fisher, Kim W.; Shogren, Karrie A.

2016-01-01

This study examined adolescents' social capital, through social network analyses (i.e., ego network analyses), in two high schools where students were placed into academic tracks adopted by the schools and shaped by disability status (i.e., general education, co-taught, segregated special education classrooms). The impact of academic tracks, as…

Convolutional networks for vehicle track segmentation

NASA Astrophysics Data System (ADS)

Quach, Tu-Thach

2017-10-01

Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times of the same scene, rely on simple and fast models to label track pixels. These models, however, are unable to capture natural track features, such as continuity and parallelism. More powerful but computationally expensive models can be used in offline settings. We present an approach that uses dilated convolutional networks consisting of a series of 3×3 convolutions to segment vehicle tracks. The design of our networks considers the fact that remote sensing applications tend to operate in low power and have limited training data. As a result, we aim for small and efficient networks that can be trained end-to-end to learn natural track features entirely from limited training data. We demonstrate that our six-layer network, trained on just 90 images, is computationally efficient and improves the F-score on a standard dataset to 0.992, up from 0.959 obtained by the current state-of-the-art method.

The scheduling of tracking times for interplanetary spacecraft on the Deep Space Network

NASA Technical Reports Server (NTRS)

Webb, W. A.

1978-01-01

The Deep Space Network (DSN) is a network of tracking stations, located throughout the globe, used to track spacecraft for NASA's interplanetary missions. This paper describes a computer program, DSNTRAK, which provides an optimum daily tracking schedule for the DSN given the view periods at each station for a mission set of n spacecraft, where n is between 2 and 6. The objective function is specified in terms of relative total daily tracking time requirements between the n spacecraft. Linear programming is used to maximize the total daily tracking time and determine an optimal daily tracking schedule consistent with DSN station capabilities. DSNTRAK is used as part of a procedure to provide DSN load forecasting information for proposed future NASA mission sets.

Virtual target tracking (VTT) as applied to mobile satellite communication networks

NASA Astrophysics Data System (ADS)

Amoozegar, Farid

1999-08-01

Traditionally, target tracking has been used for aerospace applications, such as, tracking highly maneuvering targets in a cluttered environment for missile-to-target intercept scenarios. Although the speed and maneuvering capability of current aerospace targets demand more efficient algorithms, many complex techniques have already been proposed in the literature, which primarily cover the defense applications of tracking methods. On the other hand, the rapid growth of Global Communication Systems, Global Information Systems (GIS), and Global Positioning Systems (GPS) is creating new and more diverse challenges for multi-target tracking applications. Mobile communication and computing can very well appreciate a huge market for Cellular Communication and Tracking Devices (CCTD), which will be tracking networked devices at the cellular level. The objective of this paper is to introduce a new concept, i.e., Virtual Target Tracking (VTT) for commercial applications of multi-target tracking algorithms and techniques as applied to mobile satellite communication networks. It would be discussed how Virtual Target Tracking would bring more diversity to target tracking research.

Challenges and Opportunities of Information Technology in the 90s. Track V: Managing Telecommunications and Networking.

ERIC Educational Resources Information Center

CAUSE, Boulder, CO.

Six papers from the 1990 CAUSE conference's Track V, Managing Telecommunications and Networking are presented. Topics address such subjects as network funding, support services, access to networks, improvement of instruction through networks, and image transmission. Papers and their authors are as follows: "What's New in…

GSFC network operations with Tracking and Data Relay Satellites

NASA Astrophysics Data System (ADS)

Spearing, R.; Perreten, D. E.

The Tracking and Data Relay Satellite System (TDRSS) Network (TN) has been developed to provide services to all NASA User spacecraft in near-earth orbits. Three inter-relating entities will provide these services. The TN has been transformed from a network continuously changing to meet User specific requirements to a network which is flexible to meet future needs without significant changes in operational concepts. Attention is given to the evolution of the TN network, the TN capabilities-space segment, forward link services, tracking services, return link services, the three basic capabilities, single access services, multiple access services, simulation services, the White Sands Ground Terminal, the NASA communications network, and the network control center.

GSFC network operations with Tracking and Data Relay Satellites

NASA Technical Reports Server (NTRS)

Spearing, R.; Perreten, D. E.

1984-01-01

The Tracking and Data Relay Satellite System (TDRSS) Network (TN) has been developed to provide services to all NASA User spacecraft in near-earth orbits. Three inter-relating entities will provide these services. The TN has been transformed from a network continuously changing to meet User specific requirements to a network which is flexible to meet future needs without significant changes in operational concepts. Attention is given to the evolution of the TN network, the TN capabilities-space segment, forward link services, tracking services, return link services, the three basic capabilities, single access services, multiple access services, simulation services, the White Sands Ground Terminal, the NASA communications network, and the network control center.

Convolutional networks for vehicle track segmentation

DOE PAGES

Quach, Tu-Thach

2017-08-19

Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times of the same scene, rely on simple, fast models to label track pixels. These models, however, are unable to capture natural track features such as continuity and parallelism. More powerful, but computationally expensive models can be used in offline settings. We present an approach that uses dilated convolutional networks consisting of a series of 3-by-3 convolutions to segment vehicle tracks. The design of our networks considers the fact that remote sensing applications tend to operate inmore » low power and have limited training data. As a result, we aim for small, efficient networks that can be trained end-to-end to learn natural track features entirely from limited training data. We demonstrate that our 6-layer network, trained on just 90 images, is computationally efficient and improves the F-score on a standard dataset to 0.992, up from 0.959 obtained by the current state-of-the-art method.« less

Convolutional networks for vehicle track segmentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Quach, Tu-Thach

Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times of the same scene, rely on simple, fast models to label track pixels. These models, however, are unable to capture natural track features such as continuity and parallelism. More powerful, but computationally expensive models can be used in offline settings. We present an approach that uses dilated convolutional networks consisting of a series of 3-by-3 convolutions to segment vehicle tracks. The design of our networks considers the fact that remote sensing applications tend to operate inmore » low power and have limited training data. As a result, we aim for small, efficient networks that can be trained end-to-end to learn natural track features entirely from limited training data. We demonstrate that our 6-layer network, trained on just 90 images, is computationally efficient and improves the F-score on a standard dataset to 0.992, up from 0.959 obtained by the current state-of-the-art method.« less

Robust visual tracking via multiscale deep sparse networks

NASA Astrophysics Data System (ADS)

Wang, Xin; Hou, Zhiqiang; Yu, Wangsheng; Xue, Yang; Jin, Zefenfen; Dai, Bo

2017-04-01

In visual tracking, deep learning with offline pretraining can extract more intrinsic and robust features. It has significant success solving the tracking drift in a complicated environment. However, offline pretraining requires numerous auxiliary training datasets and is considerably time-consuming for tracking tasks. To solve these problems, a multiscale sparse networks-based tracker (MSNT) under the particle filter framework is proposed. Based on the stacked sparse autoencoders and rectifier linear unit, the tracker has a flexible and adjustable architecture without the offline pretraining process and exploits the robust and powerful features effectively only through online training of limited labeled data. Meanwhile, the tracker builds four deep sparse networks of different scales, according to the target's profile type. During tracking, the tracker selects the matched tracking network adaptively in accordance with the initial target's profile type. It preserves the inherent structural information more efficiently than the single-scale networks. Additionally, a corresponding update strategy is proposed to improve the robustness of the tracker. Extensive experimental results on a large scale benchmark dataset show that the proposed method performs favorably against state-of-the-art methods in challenging environments.

Scalable Track Detection in SAR CCD Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chow, James G; Quach, Tu-Thach

Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images ta ken at different times of the same scene, rely on simple, fast models to label track pixels. These models, however, are often too simple to capture natural track features such as continuity and parallelism. We present a simple convolutional network architecture consisting of a series of 3-by-3 convolutions to detect tracks. The network is trained end-to-end to learn natural track features entirely from data. The network is computationally efficient and improves the F-score on a standard dataset to 0.988,more » up fr om 0.907 obtained by the current state-of-the-art method.« less

PRiFi Networking for Tracking-Resistant Mobile Computing

DTIC Science & Technology

2017-11-01

PRiFi NETWORKING FOR TRACKING-RESISTANT MOBILE COMPUTING YALE UNIVERSITY NOVEMBER 2017 FINAL TECHNICAL REPORT APPROVED FOR PUBLIC RELEASE...From - To) FEB 2016 – MAY 2017 4. TITLE AND SUBTITLE PRiFi NETWORKING FOR TRACKING-RESISTANT MOBILE COMPUTING 5a. CONTRACT NUMBER FA8750-16-2-0034...3 Figure 2: What We Have: A Cloud of Secret Mass Surveillance Processes .................................. 6 Figure 3: What

Distributed cluster management techniques for unattended ground sensor networks

NASA Astrophysics Data System (ADS)

Essawy, Magdi A.; Stelzig, Chad A.; Bevington, James E.; Minor, Sharon

2005-05-01

Smart Sensor Networks are becoming important target detection and tracking tools. The challenging problems in such networks include the sensor fusion, data management and communication schemes. This work discusses techniques used to distribute sensor management and multi-target tracking responsibilities across an ad hoc, self-healing cluster of sensor nodes. Although miniaturized computing resources possess the ability to host complex tracking and data fusion algorithms, there still exist inherent bandwidth constraints on the RF channel. Therefore, special attention is placed on the reduction of node-to-node communications within the cluster by minimizing unsolicited messaging, and distributing the sensor fusion and tracking tasks onto local portions of the network. Several challenging problems are addressed in this work including track initialization and conflict resolution, track ownership handling, and communication control optimization. Emphasis is also placed on increasing the overall robustness of the sensor cluster through independent decision capabilities on all sensor nodes. Track initiation is performed using collaborative sensing within a neighborhood of sensor nodes, allowing each node to independently determine if initial track ownership should be assumed. This autonomous track initiation prevents the formation of duplicate tracks while eliminating the need for a central "management" node to assign tracking responsibilities. Track update is performed as an ownership node requests sensor reports from neighboring nodes based on track error covariance and the neighboring nodes geo-positional location. Track ownership is periodically recomputed using propagated track states to determine which sensing node provides the desired coverage characteristics. High fidelity multi-target simulation results are presented, indicating the distribution of sensor management and tracking capabilities to not only reduce communication bandwidth consumption, but to also simplify multi-target tracking within the cluster.

The effect of tracking network configuration on GPS baseline estimates for the CASA Uno experiment

NASA Technical Reports Server (NTRS)

Wolf, S. Kornreich; Dixon, T. H.; Freymueller, J. T.

1990-01-01

The effect of the tracking network on long (greater than 100 km) GPS baseline estimates was estimated using various subsets of the global tracking network initiated by the first Central and South America (CASA Uno) experiment. It was found that best results could be obtained with a global tacking network consisting of three U.S. stations, two sites in the southwestern Pacific, and two sites in Europe. In comparison with smaller subsets, this global network improved the baseline repeatability, the resolution of carrier phase cycle ambiguities, and formal errors of the orbit estimates.

Count Your Calories and Share Them: Health Benefits of Sharing mHealth Information on Social Networking Sites.

PubMed

Oeldorf-Hirsch, Anne; High, Andrew C; Christensen, John L

2018-04-23

This study investigates the relationship between sharing tracked mobile health (mHealth) information online, supportive communication, feedback, and health behavior. Based on the Integrated Theory of mHealth, our model asserts that sharing tracked health information on social networking sites benefits users' perceptions of their health because of the supportive communication they gain from members of their online social networks and that the amount of feedback people receive moderates these associations. Users of mHealth apps (N = 511) completed an online survey, and results revealed that both sharing tracked health information and receiving feedback from an online social network were positively associated with supportive communication. Network support both corresponded with improved health behavior and mediated the association between sharing health information and users' health behavior. As users received greater amounts of feedback from their online social networks, however, the association between sharing tracked health information and health behavior decreased. Theoretical implications for sharing tracked health information and practical implications for using mHealth apps are discussed.

Data fusion for target tracking and classification with wireless sensor network

NASA Astrophysics Data System (ADS)

Pannetier, Benjamin; Doumerc, Robin; Moras, Julien; Dezert, Jean; Canevet, Loic

2016-10-01

In this paper, we address the problem of multiple ground target tracking and classification with information obtained from a unattended wireless sensor network. A multiple target tracking (MTT) algorithm, taking into account road and vegetation information, is proposed based on a centralized architecture. One of the key issue is how to adapt classical MTT approach to satisfy embedded processing. Based on track statistics, the classification algorithm uses estimated location, velocity and acceleration to help to classify targets. The algorithms enables tracking human and vehicles driving both on and off road. We integrate road or trail width and vegetation cover, as constraints in target motion models to improve performance of tracking under constraint with classification fusion. Our algorithm also presents different dynamic models, to palliate the maneuvers of targets. The tracking and classification algorithms are integrated into an operational platform (the fusion node). In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

Tracking of time-varying genomic regulatory networks with a LASSO-Kalman smoother

PubMed Central

2014-01-01

It is widely accepted that cellular requirements and environmental conditions dictate the architecture of genetic regulatory networks. Nonetheless, the status quo in regulatory network modeling and analysis assumes an invariant network topology over time. In this paper, we refocus on a dynamic perspective of genetic networks, one that can uncover substantial topological changes in network structure during biological processes such as developmental growth. We propose a novel outlook on the inference of time-varying genetic networks, from a limited number of noisy observations, by formulating the network estimation as a target tracking problem. We overcome the limited number of observations (small n large p problem) by performing tracking in a compressed domain. Assuming linear dynamics, we derive the LASSO-Kalman smoother, which recursively computes the minimum mean-square sparse estimate of the network connectivity at each time point. The LASSO operator, motivated by the sparsity of the genetic regulatory networks, allows simultaneous signal recovery and compression, thereby reducing the amount of required observations. The smoothing improves the estimation by incorporating all observations. We track the time-varying networks during the life cycle of the Drosophila melanogaster. The recovered networks show that few genes are permanent, whereas most are transient, acting only during specific developmental phases of the organism. PMID:24517200

Probabilistic track coverage in cooperative sensor networks.

PubMed

Ferrari, Silvia; Zhang, Guoxian; Wettergren, Thomas A

2010-12-01

The quality of service of a network performing cooperative track detection is represented by the probability of obtaining multiple elementary detections over time along a target track. Recently, two different lines of research, namely, distributed-search theory and geometric transversals, have been used in the literature for deriving the probability of track detection as a function of random and deterministic sensors' positions, respectively. In this paper, we prove that these two approaches are equivalent under the same problem formulation. Also, we present a new performance function that is derived by extending the geometric-transversal approach to the case of random sensors' positions using Poisson flats. As a result, a unified approach for addressing track detection in both deterministic and probabilistic sensor networks is obtained. The new performance function is validated through numerical simulations and is shown to bring about considerable computational savings for both deterministic and probabilistic sensor networks.

Real-Time Adaptation of Decision Thresholds in Sensor Networks for Detection of Moving Targets (PREPRINT)

DTIC Science & Technology

2010-01-01

target kinematics for multiple sensor detections is referred to as the track - before - detect strategy, and is commonly adopted in multi-sensor surveillance...of moving targets. Wettergren [4] presented an application of track - before - detect strategies to undersea distributed sensor networks. In de- signing...the deployment of a distributed passive sensor network that employs this track - before - detect procedure, it is impera- tive that the placement of

Enhanced online convolutional neural networks for object tracking

NASA Astrophysics Data System (ADS)

Zhang, Dengzhuo; Gao, Yun; Zhou, Hao; Li, Tianwen

2018-04-01

In recent several years, object tracking based on convolution neural network has gained more and more attention. The initialization and update of convolution filters can directly affect the precision of object tracking effective. In this paper, a novel object tracking via an enhanced online convolution neural network without offline training is proposed, which initializes the convolution filters by a k-means++ algorithm and updates the filters by an error back-propagation. The comparative experiments of 7 trackers on 15 challenging sequences showed that our tracker can perform better than other trackers in terms of AUC and precision.

The Deep Space Network

NASA Technical Reports Server (NTRS)

1979-01-01

Deep Space Network progress in flight project support, tracking and data acquisition, research and technology, network engineering, hardware and software implementation, and operations is cited. Topics covered include: tracking and ground based navigation; spacecraft/ground communication; station control and operations technology; ground communications; and deep space stations.

Optimal Quantization Scheme for Data-Efficient Target Tracking via UWSNs Using Quantized Measurements.

PubMed

Zhang, Senlin; Chen, Huayan; Liu, Meiqin; Zhang, Qunfei

2017-11-07

Target tracking is one of the broad applications of underwater wireless sensor networks (UWSNs). However, as a result of the temporal and spatial variability of acoustic channels, underwater acoustic communications suffer from an extremely limited bandwidth. In order to reduce network congestion, it is important to shorten the length of the data transmitted from local sensors to the fusion center by quantization. Although quantization can reduce bandwidth cost, it also brings about bad tracking performance as a result of information loss after quantization. To solve this problem, this paper proposes an optimal quantization-based target tracking scheme. It improves the tracking performance of low-bit quantized measurements by minimizing the additional covariance caused by quantization. The simulation demonstrates that our scheme performs much better than the conventional uniform quantization-based target tracking scheme and the increment of the data length affects our scheme only a little. Its tracking performance improves by only 4.4% from 2- to 3-bit, which means our scheme weakly depends on the number of data bits. Moreover, our scheme also weakly depends on the number of participate sensors, and it can work well in sparse sensor networks. In a 6 × 6 × 6 sensor network, compared with 4 × 4 × 4 sensor networks, the number of participant sensors increases by 334.92%, while the tracking accuracy using 1-bit quantized measurements improves by only 50.77%. Overall, our optimal quantization-based target tracking scheme can achieve the pursuit of data-efficiency, which fits the requirements of low-bandwidth UWSNs.

Fuzzy Neural Network-Based Interacting Multiple Model for Multi-Node Target Tracking Algorithm

PubMed Central

Sun, Baoliang; Jiang, Chunlan; Li, Ming

2016-01-01

An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjusted during the multiple model interacting output stage using the difference between the theoretical and estimated values of the measured error covariance matrix. The FNN fusion system was established during multi-node fusion to integrate with the target state estimated data from different nodes and consequently obtain network target state estimation. The feasibility of the algorithm was verified based on a network of nine detection nodes. Experimental results indicated that the proposed algorithm could trace the maneuvering target effectively under sensor failure and unknown system measurement errors. The proposed algorithm exhibited great practicability in the multi-node target tracking of WSNs. PMID:27809271

Comparison of home and away-from-home physical activity using accelerometers and cellular network-based tracking devices.

PubMed

Ramulu, Pradeep Y; Chan, Emilie S; Loyd, Tara L; Ferrucci, Luigi; Friedman, David S

2012-08-01

Measuring physical at home and away from home is essential for assessing health and well-being, and could help design interventions to increase physical activity. Here, we describe how physical activity at home and away from home can be quantified by combining information from cellular network-based tracking devices and accelerometers. Thirty-five working adults wore a cellular network-based tracking device and an accelerometer for 6 consecutive days and logged their travel away from home. Performance of the tracking device was determined using the travel log for reference. Tracking device and accelerometer data were merged to compare physical activity at home and away from home. The tracking device detected 98.6% of all away-from-home excursions, accurately measured time away from home and demonstrated few prolonged signal drop-out periods. Most physical activity took place away from home on weekdays, but not on weekends. Subjects were more physically active per unit of time while away from home, particularly on weekends. Cellular network-based tracking devices represent an alternative to global positioning systems for tracking location, and provide information easily integrated with accelerometers to determine where physical activity takes place. Promoting greater time spent away from home may increase physical activity.

A neural network z-vertex trigger for Belle II

NASA Astrophysics Data System (ADS)

Neuhaus, S.; Skambraks, S.; Abudinen, F.; Chen, Y.; Feindt, M.; Frühwirth, R.; Heck, M.; Kiesling, C.; Knoll, A.; Paul, S.; Schieck, J.

2015-05-01

We present the concept of a track trigger for the Belle II experiment, based on a neural network approach, that is able to reconstruct the z (longitudinal) position of the event vertex within the latency of the first level trigger. The trigger will thus be able to suppress a large fraction of the dominating background from events outside of the interaction region. The trigger uses the drift time information of the hits from the Central Drift Chamber (CDC) of Belle II within narrow cones in polar and azimuthal angle as well as in transverse momentum (sectors), and estimates the z-vertex without explicit track reconstruction. The preprocessing for the track trigger is based on the track information provided by the standard CDC trigger. It takes input from the 2D (r — φ) track finder, adds information from the stereo wires of the CDC, and finds the appropriate sectors in the CDC for each track in a given event. Within each sector, the z-vertex of the associated track is estimated by a specialized neural network, with a continuous output corresponding to the scaled z-vertex. The input values for the neural network are calculated from the wire hits of the CDC.

Siamese convolutional networks for tracking the spine motion

NASA Astrophysics Data System (ADS)

Liu, Yuan; Sui, Xiubao; Sun, Yicheng; Liu, Chengwei; Hu, Yong

2017-09-01

Deep learning models have demonstrated great success in various computer vision tasks such as image classification and object tracking. However, tracking the lumbar spine by digitalized video fluoroscopic imaging (DVFI), which can quantitatively analyze the motion mode of spine to diagnose lumbar instability, has not yet been well developed due to the lack of steady and robust tracking method. In this paper, we propose a novel visual tracking algorithm of the lumbar vertebra motion based on a Siamese convolutional neural network (CNN) model. We train a full-convolutional neural network offline to learn generic image features. The network is trained to learn a similarity function that compares the labeled target in the first frame with the candidate patches in the current frame. The similarity function returns a high score if the two images depict the same object. Once learned, the similarity function is used to track a previously unseen object without any adapting online. In the current frame, our tracker is performed by evaluating the candidate rotated patches sampled around the previous frame target position and presents a rotated bounding box to locate the predicted target precisely. Results indicate that the proposed tracking method can detect the lumbar vertebra steadily and robustly. Especially for images with low contrast and cluttered background, the presented tracker can still achieve good tracking performance. Further, the proposed algorithm operates at high speed for real time tracking.

Precise Orbit Determination of BeiDou Navigation Satellite System

NASA Astrophysics Data System (ADS)

He, Lina; Ge, Maorong; Wang, Jiexian; Wickert, Jens; Schuh, Harald

2013-04-01

China has been developing its own independent satellite navigation system since decades. Now the COMPASS system, also known as BeiDou, is emerging and gaining more and more interest and attention in the worldwide GNSS communities. The current regional BeiDou system is ready for its operational service around the end of 2012 with a constellation including five Geostationary Earth Orbit satellites (GEO), five Inclined Geosynchronous Orbit satellites (IGSO) and four Medium Earth orbit (MEO) satellites in operation. Besides the open service with positioning accuracy of around 10m which is free to civilian users, both precise relative positioning, and precise point positioning are demonstrated as well. In order to enhance the BeiDou precise positioning service, Precise Orbit Determination (POD) which is essential of any satellite navigation system has been investigated and studied thoroughly. To further improving the orbits of different types of satellites, we study the impact of network coverage on POD data products by comparing results from tracking networks over the Chinese territory, Asian-Pacific, Asian and of global scale. Furthermore, we concentrate on the improvement of involving MEOs on the orbit quality of GEOs and IGSOs. POD with and without MEOs are undertaken and results are analyzed. Finally, integer ambiguity resolution which brings highly improvement on orbits and positions with GPS data is also carried out and its effect on POD data products is assessed and discussed in detail. Seven weeks of BeiDou data from a ground tracking network, deployed by Wuhan University is employed in this study. The test constellation includes four GEO, five IGSO and two MEO satellites in operation. The three-day solution approach is employed to enhance its strength due to the limited coverage of the tracking network and the small movement of most of the satellites. A number of tracking scenarios and processing schemas are identified and processed and overlapping orbit differences are utilized to qualify the estimated orbits and clocks. The results show that GEO orbits, especially the along-track component, can be significantly improved by extending the tracking network in China along longitude direction, whereas IGSOs gain more improvement if the tracking network extends in latitude. For the current tracking network, deploying tracking stations on the eastern side, for example in New Zealand and/or in Hawaii, will significantly reduce along-track biases of GEOs on the same side. The involvement of MEOs and ambiguity-fixing also make the orbits better but rather moderate. Key words: BeiDou, precise orbit determination (POD), tracking network, ambiguity-fixing

Historics of the Space Tracking And Data Acquisition Network (STADAN), the Manned Space Flight Network (MSFN), and the NASA Communications Network (NASCOM)

NASA Technical Reports Server (NTRS)

Corliss, W. R.

1974-01-01

The historical and technical aspects of the major networks which comprise the NASA tracking and data acquisition system are considered in a complete reference work which traces the origin and growth of STADAN, MSFN, and NASCOM up to mid-1971. The roles of these networks in both the Gemini and Apollo programs are discussed, and the separate developmental trends are identified for each network.

Satellite-tracking and earth-dynamics research programs. [geodetic and geophysical investigations and atmospheric research using satellite drag data

NASA Technical Reports Server (NTRS)

1972-01-01

Satellite tracking and earth dynamics research programs are discussed. Geodetic and geophysical investigations are reported along with atmospheric research using satellite drag data. Satellite tracking network functions and support groups which are discussed include: network operations, communications, data-services division, moonwatch, and programming group.

The Ethnic Dimensions of Social Capital: How Parental Networks Shape Track Placement in Germany.

ERIC Educational Resources Information Center

Werum, Regina E.

This research examined the relationship between parental social capital and children's educational track placement in Germany, and how parental social capital differentially affected the tracking experiences of German and non-German children. Parental social capital was defined as the degree to which adults used family networks or connections to…

Algebraic Approach for Recovering Topology in Distributed Camera Networks

DTIC Science & Technology

2009-01-14

not valid for camera networks. Spatial sam- pling of plenoptic function [2] from a network of cameras is rarely i.i.d. (independent and identi- cally...coverage can be used to track and compare paths in a wireless camera network without any metric calibration information. In particular, these results can...edition edition, 2000. [14] A. Rahimi, B. Dunagan, and T. Darrell. Si- multaneous calibration and tracking with a network of non-overlapping sensors. In

H∞ output tracking control of discrete-time nonlinear systems via standard neural network models.

PubMed

Liu, Meiqin; Zhang, Senlin; Chen, Haiyang; Sheng, Weihua

2014-10-01

This brief proposes an output tracking control for a class of discrete-time nonlinear systems with disturbances. A standard neural network model is used to represent discrete-time nonlinear systems whose nonlinearity satisfies the sector conditions. H∞ control performance for the closed-loop system including the standard neural network model, the reference model, and state feedback controller is analyzed using Lyapunov-Krasovskii stability theorem and linear matrix inequality (LMI) approach. The H∞ controller, of which the parameters are obtained by solving LMIs, guarantees that the output of the closed-loop system closely tracks the output of a given reference model well, and reduces the influence of disturbances on the tracking error. Three numerical examples are provided to show the effectiveness of the proposed H∞ output tracking design approach.

Non-Intrusive Gaze Tracking Using Artificial Neural Networks

DTIC Science & Technology

1994-01-05

We have developed an artificial neural network based gaze tracking, system which can be customized to individual users. A three layer feed forward...empirical analysis of the performance of a large number of artificial neural network architectures for this task. Suggestions for further explorations...for neurally based gaze trackers are presented, and are related to other similar artificial neural network applications such as autonomous road following.

Track vertex reconstruction with neural networks at the first level trigger of Belle II

NASA Astrophysics Data System (ADS)

Neuhaus, Sara; Skambraks, Sebastian; Kiesling, Christian

2017-08-01

The track trigger is one of the main components of the Belle II first level trigger, taking input from the Central Drift Chamber (CDC). It consists of several stages, first combining hits to track segments, followed by a 2D track finding in the transverse plane and finally a 3D track reconstruction. The results of the track trigger are the track multiplicity, the momentum vector of each track and the longitudinal displacement of the origin or production vertex of each track ("z-vertex"). The latter allows to reject background tracks from outside of the interaction region and thus to suppress a large fraction of the machine background. This contribution focuses on the track finding stage using Hough transforms and on the z-vertex reconstruction with neural networks. We describe the algorithms and show performance studies on simulated events.

Message Mode Operations for Spacecraft: A Proposal for Operating Spacecraft During Cruise and Mitigating the Network Loading Crunch

NASA Technical Reports Server (NTRS)

Greenberg, Ed; MacMedan, Marv; Kazz, Greg; Kallemeyn, Pieter

2000-01-01

The NASA Deep Space Network (DSN) is a world-class spacecraft tracking facility with stations located in Spain, Australia and USA, servicing Deep Space Missions of many space agencies. The current system of scheduling spacecraft during cruise for multiple 8 hour tracking sessions per week currently leads to an overcommitted DSN. Studies indicate that future projected mission demands upon the Network will only make the loading problem worse. Therefore, a more efficient scheduling of DSN resources is necessary in order to support the additional network loading envisioned in the next few years: The number of missions is projected to increase from 25 in 1998 to 34 by 2001. In fact given the challenge of the NASA administrator, Dan Goldin, of launching 12 spacecraft per year, the DSN would be tracking approximately 90 spacecraft by 2010. Currently a large amount of antenna time and network resources are subscribed by a project in order to have their mission supported during the cruise phase. The recently completed Mars Pathfinder mission was tracked 3 times a week (8 hours/day) during the majority of its cruise to Mars. This paper proposes an innovative approach called Message Mode Operations (MMO) for mitigating the Network loading problem while continuing to meet the tracking, reporting, time management, and scheduling requirements of these missions during Cruise while occupying very short tracking times. MMO satisfies these requirements by providing the following services: Spacecraft Health and Welfare Monitoring Service Command Delivery Service Adaptive Spacecraft Scheduling Service Orbit Determination Service Time Calibration Service Utilizing more efficient engineering telemetry summarization and filtering techniques on-board the spacecraft and collapsing the navigation requirements for Doppler and Range into shorter tracks, we believe spacecraft can be adequately serviced using short 10 to 30 minute tracking sessions. This claim assumes that certain changes would have to he made in the way the Network traditionally services missions in Cruise. Furthermore, limiting spacecraft to short sessions will free up larger blocks of time in the tracking schedule to help accommodate future tracking demands soon to be placed upon the Network. This paper describes the key characteristics and benefits of MMO, the operational scenarios for its use, the required changes to the ground system in order to make this approach feasible and the results of two simulations: 1) to determine the effects of MMO on projected mission loading on the DSN and, 2) to determine the effect MMO has on spacecraft orbit determination.

The deep space network. [tracking and communication support for space probes

NASA Technical Reports Server (NTRS)

1974-01-01

The objectives, functions, and organization of the deep space network are summarized. Progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is reported. Interface support for the Mariner Venus Mercury 1973 flight and Pioneer 10 and 11 missions is included.

Fin whale tracks recorded by a seismic network on the Juan de Fuca Ridge, Northeast Pacific Ocean.

PubMed

Soule, Dax C; Wilcock, William S D

2013-03-01

Fin whale calls recorded from 2003 to 2004 by a seafloor seismic network on the Endeavour segment of the Juan de Fuca Ridge were analyzed to determine tracks and calling patterns. Over 150 tracks were obtained with a total duration of ~800 h and swimming speeds from 1 to 12 km/h. The dominant inter-pulse interval (IPI) is 24 s and the IPI patterns define 4 categories: a 25 s single IPI and 24/30 s dual IPI produced by single calling whales, a 24/13 s dual IPI interpreted as two calling whales, and an irregular IPI interpreted as groups of calling whales. There are also tracks in which the IPI switches between categories. Call rates vary seasonally with all the tracks between August and April. From August to October tracks are dominated by the irregular IPI and are predominantly headed to the northwest, suggesting that a portion of the fin whale population does not migrate south in the fall. The other IPI categories occur primarily from November to March. These tracks have slower swimming speeds, tend to meander, and are predominantly to the south. The distribution of fin whales around the network is non-random with more calls near the network and to the east and north.

ESAM: Endocrine inspired Sensor Activation Mechanism for multi-target tracking in WSNs

NASA Astrophysics Data System (ADS)

Adil Mahdi, Omar; Wahab, Ainuddin Wahid Abdul; Idris, Mohd Yamani Idna; Znaid, Ammar Abu; Khan, Suleman; Al-Mayouf, Yusor Rafid Bahar

2016-10-01

Target tracking is a significant application of wireless sensor networks (WSNs) in which deployment of self-organizing and energy efficient algorithms is required. The tracking accuracy increases as more sensor nodes are activated around the target but more energy is consumed. Thus, in this study, we focus on limiting the number of sensors by forming an ad-hoc network that operates autonomously. This will reduce the energy consumption and prolong the sensor network lifetime. In this paper, we propose a fully distributed algorithm, an Endocrine inspired Sensor Activation Mechanism for multi target-tracking (ESAM) which reflecting the properties of real life sensor activation system based on the information circulating principle in the endocrine system of the human body. Sensor nodes in our network are secreting different hormones according to certain rules. The hormone level enables the nodes to regulate an efficient sleep and wake up cycle of nodes to reduce the energy consumption. It is evident from the simulation results that the proposed ESAM in autonomous sensor network exhibits a stable performance without the need of commands from a central controller. Moreover, the proposed ESAM generates more efficient and persistent results as compared to other algorithms for tracking an invading object.

Tracking trade transactions in water resource systems: A node-arc optimization formulation

NASA Astrophysics Data System (ADS)

Erfani, Tohid; Huskova, Ivana; Harou, Julien J.

2013-05-01

We formulate and apply a multicommodity network flow node-arc optimization model capable of tracking trade transactions in complex water resource systems. The model uses a simple node to node network connectivity matrix and does not require preprocessing of all possible flow paths in the network. We compare the proposed node-arc formulation with an existing arc-path (flow path) formulation and explain the advantages and difficulties of both approaches. We verify the proposed formulation model on a hypothetical water distribution network. Results indicate the arc-path model solves the problem with fewer constraints, but the proposed formulation allows using a simple network connectivity matrix which simplifies modeling large or complex networks. The proposed algorithm allows converting existing node-arc hydroeconomic models that broadly represent water trading to ones that also track individual supplier-receiver relationships (trade transactions).

Identifying and tracking dynamic processes in social networks

NASA Astrophysics Data System (ADS)

Chung, Wayne; Savell, Robert; Schütt, Jan-Peter; Cybenko, George

2006-05-01

The detection and tracking of embedded malicious subnets in an active social network can be computationally daunting due to the quantity of transactional data generated in the natural interaction of large numbers of actors comprising a network. In addition, detection of illicit behavior may be further complicated by evasive strategies designed to camouflage the activities of the covert subnet. In this work, we move beyond traditional static methods of social network analysis to develop a set of dynamic process models which encode various modes of behavior in active social networks. These models will serve as the basis for a new application of the Process Query System (PQS) to the identification and tracking of covert dynamic processes in social networks. We present a preliminary result from application of our technique in a real-world data stream-- the Enron email corpus.

GPS NAVSTAR-4 and NTS-2 Long Term Frequency Stability and Time Transfer Analysis.

DTIC Science & Technology

1980-06-30

delta pseudo-range, are taken every 6 s. NTS Tracking Network Figure 4 presents the four station network employed for tracking the NTS spacecraft. Thc ...limits of visibility for the NRL CBD (Chesapeake Bay Division), Panama, Australia, and Eng- land tracking stations are depicted by the symbols C, P...GIAT and, in Australia, with the Division of National Mapping. The CBD tracking station had port- able clock and TV links to the U.S. Naval Observatory

Good Features to Correlate for Visual Tracking

NASA Astrophysics Data System (ADS)

Gundogdu, Erhan; Alatan, A. Aydin

2018-05-01

During the recent years, correlation filters have shown dominant and spectacular results for visual object tracking. The types of the features that are employed in these family of trackers significantly affect the performance of visual tracking. The ultimate goal is to utilize robust features invariant to any kind of appearance change of the object, while predicting the object location as properly as in the case of no appearance change. As the deep learning based methods have emerged, the study of learning features for specific tasks has accelerated. For instance, discriminative visual tracking methods based on deep architectures have been studied with promising performance. Nevertheless, correlation filter based (CFB) trackers confine themselves to use the pre-trained networks which are trained for object classification problem. To this end, in this manuscript the problem of learning deep fully convolutional features for the CFB visual tracking is formulated. In order to learn the proposed model, a novel and efficient backpropagation algorithm is presented based on the loss function of the network. The proposed learning framework enables the network model to be flexible for a custom design. Moreover, it alleviates the dependency on the network trained for classification. Extensive performance analysis shows the efficacy of the proposed custom design in the CFB tracking framework. By fine-tuning the convolutional parts of a state-of-the-art network and integrating this model to a CFB tracker, which is the top performing one of VOT2016, 18% increase is achieved in terms of expected average overlap, and tracking failures are decreased by 25%, while maintaining the superiority over the state-of-the-art methods in OTB-2013 and OTB-2015 tracking datasets.

Terminal Sliding Mode-Based Consensus Tracking Control for Networked Uncertain Mechanical Systems on Digraphs.

PubMed

Chen, Gang; Song, Yongduan; Guan, Yanfeng

2018-03-01

This brief investigates the finite-time consensus tracking control problem for networked uncertain mechanical systems on digraphs. A new terminal sliding-mode-based cooperative control scheme is developed to guarantee that the tracking errors converge to an arbitrarily small bound around zero in finite time. All the networked systems can have different dynamics and all the dynamics are unknown. A neural network is used at each node to approximate the local unknown dynamics. The control schemes are implemented in a fully distributed manner. The proposed control method eliminates some limitations in the existing terminal sliding-mode-based consensus control methods and extends the existing analysis methods to the case of directed graphs. Simulation results on networked robot manipulators are provided to show the effectiveness of the proposed control algorithms.

Decentralized cooperative TOA/AOA target tracking for hierarchical wireless sensor networks.

PubMed

Chen, Ying-Chih; Wen, Chih-Yu

2012-11-08

This paper proposes a distributed method for cooperative target tracking in hierarchical wireless sensor networks. The concept of leader-based information processing is conducted to achieve object positioning, considering a cluster-based network topology. Random timers and local information are applied to adaptively select a sub-cluster for the localization task. The proposed energy-efficient tracking algorithm allows each sub-cluster member to locally estimate the target position with a Bayesian filtering framework and a neural networking model, and further performs estimation fusion in the leader node with the covariance intersection algorithm. This paper evaluates the merits and trade-offs of the protocol design towards developing more efficient and practical algorithms for object position estimation.

Robust Target Tracking with Multi-Static Sensors under Insufficient TDOA Information.

PubMed

Shin, Hyunhak; Ku, Bonhwa; Nelson, Jill K; Ko, Hanseok

2018-05-08

This paper focuses on underwater target tracking based on a multi-static sonar network composed of passive sonobuoys and an active ping. In the multi-static sonar network, the location of the target can be estimated using TDOA (Time Difference of Arrival) measurements. However, since the sensor network may obtain insufficient and inaccurate TDOA measurements due to ambient noise and other harsh underwater conditions, target tracking performance can be significantly degraded. We propose a robust target tracking algorithm designed to operate in such a scenario. First, track management with track splitting is applied to reduce performance degradation caused by insufficient measurements. Second, a target location is estimated by a fusion of multiple TDOA measurements using a Gaussian Mixture Model (GMM). In addition, the target trajectory is refined by conducting a stack-based data association method based on multiple-frames measurements in order to more accurately estimate target trajectory. The effectiveness of the proposed method is verified through simulations.

Planetary Radio Interferometry and Doppler Experiment (PRIDE) technique: A test case of the Mars Express Phobos Flyby. II. Doppler tracking: Formulation of observed and computed values, and noise budget

NASA Astrophysics Data System (ADS)

Bocanegra-Bahamón, T. M.; Molera Calvés, G.; Gurvits, L. I.; Duev, D. A.; Pogrebenko, S. V.; Cimò, G.; Dirkx, D.; Rosenblatt, P.

2018-01-01

Context. Closed-loop Doppler data obtained by deep space tracking networks, such as the NASA Deep Space Network (DSN) and the ESA tracking station network (Estrack), are routinely used for navigation and science applications. By shadow tracking the spacecraft signal, Earth-based radio telescopes involved in the Planetary Radio Interferometry and Doppler Experiment (PRIDE) can provide open-loop Doppler tracking data only when the dedicated deep space tracking facilities are operating in closed-loop mode. Aims: We explain the data processing pipeline in detail and discuss the capabilities of the technique and its potential applications in planetary science. Methods: We provide the formulation of the observed and computed values of the Doppler data in PRIDE tracking of spacecraft and demonstrate the quality of the results using an experiment with the ESA Mars Express spacecraft as a test case. Results: We find that the Doppler residuals and the corresponding noise budget of the open-loop Doppler detections obtained with the PRIDE stations compare to the closed-loop Doppler detections obtained with dedicated deep space tracking facilities.

Optical track width measurements below 100 nm using artificial neural networks

NASA Astrophysics Data System (ADS)

Smith, R. J.; See, C. W.; Somekh, M. G.; Yacoot, A.; Choi, E.

2005-12-01

This paper discusses the feasibility of using artificial neural networks (ANNs), together with a high precision scanning optical profiler, to measure very fine track widths that are considerably below the conventional diffraction limit of a conventional optical microscope. The ANN is trained using optical profiles obtained from tracks of known widths, the network is then assessed by applying it to test profiles. The optical profiler is an ultra-stable common path scanning interferometer, which provides extremely precise surface measurements. Preliminary results, obtained with a 0.3 NA objective lens and a laser wavelength of 633 nm, show that the system is capable of measuring a 50 nm track width, with a standard deviation less than 4 nm.

Tracking and data system support for the Viking 1975 mission to Mars. Volume 1: Prelaunch planning, implementation, and testing

NASA Technical Reports Server (NTRS)

Mudgway, D. J.; Traxler, M. R.

1977-01-01

The tracking and data acquisition support for the 1975 Viking Missions to Mars is described. The history of the effort from its inception in late 1968 through the launches of Vikings 1 and 2 from Cape Kennedy in August and September 1975 is given. The Viking mission requirements for tracking and data acquisition support in both the near earth and deep space phases involved multiple radar tracking and telemetry stations, and communications networks together with the global network of tracking stations, communications, and control center. The planning, implementation, testing and management of the program are presented.

Joint Sensing/Sampling Optimization for Surface Drifting Mine Detection with High-Resolution Drift Model

DTIC Science & Technology

2012-09-01

as potential tools for large area detection coverage while being moderately inexpensive (Wettergren, Performance of Search via Track - Before - Detect for...via Track - Before - Detect for Distribute 34 Sensor Networks, 2008). These statements highlight three specific needs to further sensor network research...Bay hydrography. Journal of Marine Systems, 12, 221–236. Wettergren, T. A. (2008). Performance of search via track - before - detect for distributed

Detection of micro gap weld joint by using magneto-optical imaging and Kalman filtering compensated with RBF neural network

NASA Astrophysics Data System (ADS)

Gao, Xiangdong; Chen, Yuquan; You, Deyong; Xiao, Zhenlin; Chen, Xiaohui

2017-02-01

An approach for seam tracking of micro gap weld whose width is less than 0.1 mm based on magneto optical (MO) imaging technique during butt-joint laser welding of steel plates is investigated. Kalman filtering(KF) technology with radial basis function(RBF) neural network for weld detection by an MO sensor was applied to track the weld center position. Because the laser welding system process noises and the MO sensor measurement noises were colored noises, the estimation accuracy of traditional KF for seam tracking was degraded by the system model with extreme nonlinearities and could not be solved by the linear state-space model. Also, the statistics characteristics of noises could not be accurately obtained in actual welding. Thus, a RBF neural network was applied to the KF technique to compensate for the weld tracking errors. The neural network can restrain divergence filter and improve the system robustness. In comparison of traditional KF algorithm, the RBF with KF was not only more effectively in improving the weld tracking accuracy but also reduced noise disturbance. Experimental results showed that magneto optical imaging technique could be applied to detect micro gap weld accurately, which provides a novel approach for micro gap seam tracking.

Domain-General Brain Regions Do Not Track Linguistic Input as Closely as Language-Selective Regions.

PubMed

Blank, Idan A; Fedorenko, Evelina

2017-10-11

Language comprehension engages a cortical network of left frontal and temporal regions. Activity in this network is language-selective, showing virtually no modulation by nonlinguistic tasks. In addition, language comprehension engages a second network consisting of bilateral frontal, parietal, cingulate, and insular regions. Activity in this "multiple demand" (MD) network scales with comprehension difficulty, but also with cognitive effort across a wide range of nonlinguistic tasks in a domain-general fashion. Given the functional dissociation between the language and MD networks, their respective contributions to comprehension are likely distinct, yet such differences remain elusive. Prior neuroimaging studies have suggested that activity in each network covaries with some linguistic features that, behaviorally, influence on-line processing and comprehension. This sensitivity of the language and MD networks to local input characteristics has often been interpreted, implicitly or explicitly, as evidence that both networks track linguistic input closely, and in a manner consistent across individuals. Here, we used fMRI to directly test this assumption by comparing the BOLD signal time courses in each network across different people ( n = 45, men and women) listening to the same story. Language network activity showed fewer individual differences, indicative of closer input tracking, whereas MD network activity was more idiosyncratic and, moreover, showed lower reliability within an individual across repetitions of a story. These findings constrain cognitive models of language comprehension by suggesting a novel distinction between the processes implemented in the language and MD networks. SIGNIFICANCE STATEMENT Language comprehension recruits both language-specific mechanisms and domain-general mechanisms that are engaged in many cognitive processes. In the human cortex, language-selective mechanisms are implemented in the left-lateralized "core language network", whereas domain-general mechanisms are implemented in the bilateral "multiple demand" (MD) network. Here, we report the first direct comparison of the respective contributions of these networks to naturalistic story comprehension. Using a novel combination of neuroimaging approaches we find that MD regions track stories less closely than language regions. This finding constrains the possible contributions of the MD network to comprehension, contrasts with accounts positing that this network has continuous access to linguistic input, and suggests a new typology of comprehension processes based on their extent of input tracking. Copyright © 2017 the authors 0270-6474/17/3710000-13$15.00/0.

Technology forecasting for space communication. [analysis of systems for application to Spacecraft Data and Tracking Network

NASA Technical Reports Server (NTRS)

1973-01-01

A study was conducted to determine techniques for application to space communication. The subjects considered are as follows: (1) optical communication systems, (2) laser communications for data acquisition networks, (3) spacecraft data rate requirements, (4) telemetry, command, and data handling, (5) spacecraft tracking and data network antenna and preamplifier cost tradeoff study, and (6) spacecraft communication terminal evaluation.

Enhanced networks operations using the X Window System

NASA Technical Reports Server (NTRS)

Linares, Irving

1993-01-01

We propose an X Window Graphical User Interface (GUI) which is tailored to the operations of NASA GSFC's Network Control Center (NCC), the NASA Ground Terminal (NGT), the White Sands Ground Terminal (WSGT), and the Second Tracking and Data Relay Satellite System (TDRSS) Ground Terminal (STGT). The proposed GUI can also be easily extended to other Ground Network (GN) Tracking Stations due to its standardized nature.

Lessons Learned From the Environmental Public Health Tracking Sub-County Data Pilot Project.

PubMed

Werner, Angela K; Strosnider, Heather; Kassinger, Craig; Shin, Mikyong

2017-12-07

Small area data are key to better understanding the complex relationships between environmental health, health outcomes, and risk factors at a local level. In 2014, the Centers for Disease Control and Prevention's National Environmental Public Health Tracking Program (Tracking Program) conducted the Sub-County Data Pilot Project with grantees to consider integration of sub-county data into the National Environmental Public Health Tracking Network (Tracking Network). The Tracking Program and grantees developed sub-county-level data for several data sets during this pilot project, working to standardize processes for submitting data and creating required geographies. Grantees documented challenges they encountered during the pilot project and documented decisions. This article covers the challenges revealed during the project. It includes insights into geocoding, aggregation, population estimates, and data stability and provides recommendations for moving forward. National standards for generating, analyzing, and sharing sub-county data should be established to build a system of sub-county data that allow for comparison of outcomes, geographies, and time. Increasing the availability and accessibility of small area data will not only enhance the Tracking Network's capabilities but also contribute to an improved understanding of environmental health and informed decision making at a local level.

Indoor Trajectory Tracking Scheme Based on Delaunay Triangulation and Heuristic Information in Wireless Sensor Networks.

PubMed

Qin, Junping; Sun, Shiwen; Deng, Qingxu; Liu, Limin; Tian, Yonghong

2017-06-02

Object tracking and detection is one of the most significant research areas for wireless sensor networks. Existing indoor trajectory tracking schemes in wireless sensor networks are based on continuous localization and moving object data mining. Indoor trajectory tracking based on the received signal strength indicator ( RSSI ) has received increased attention because it has low cost and requires no special infrastructure. However, RSSI tracking introduces uncertainty because of the inaccuracies of measurement instruments and the irregularities (unstable, multipath, diffraction) of wireless signal transmissions in indoor environments. Heuristic information includes some key factors for trajectory tracking procedures. This paper proposes a novel trajectory tracking scheme based on Delaunay triangulation and heuristic information (TTDH). In this scheme, the entire field is divided into a series of triangular regions. The common side of adjacent triangular regions is regarded as a regional boundary. Our scheme detects heuristic information related to a moving object's trajectory, including boundaries and triangular regions. Then, the trajectory is formed by means of a dynamic time-warping position-fingerprint-matching algorithm with heuristic information constraints. Field experiments show that the average error distance of our scheme is less than 1.5 m, and that error does not accumulate among the regions.

An automated method for the evaluation of the pointing accuracy of sun-tracking devices

NASA Astrophysics Data System (ADS)

Baumgartner, Dietmar J.; Rieder, Harald E.; Pötzi, Werner; Freislich, Heinrich; Strutzmann, Heinz

2016-04-01

The accuracy of measurements of solar radiation (direct and diffuse radiation) depends significantly on the accuracy of the operational sun-tracking device. Thus rigid targets for instrument performance and operation are specified for international monitoring networks, such as e.g., the Baseline Surface Radiation Network (BSRN) operating under the auspices of the World Climate Research Program (WCRP). Sun-tracking devices fulfilling these accuracy targets are available from various instrument manufacturers, however none of the commercially available systems comprises a secondary accuracy control system, allowing platform operators to independently validate the pointing accuracy of sun-tracking sensors during operation. Here we present KSO-STREAMS (KSO-SunTRackEr Accuracy Monitoring System), a fully automated, system independent and cost-effective method for evaluating the pointing accuracy of sun-tracking devices. We detail the monitoring system setup, its design and specifications and results from its application to the sun-tracking system operated at the Austrian RADiation network (ARAD) site Kanzelhöhe Observatory (KSO). Results from KSO-STREAMS (for mid-March to mid-June 2015) show that the tracking accuracy of the device operated at KSO lies well within BSRN specifications (i.e. 0.1 degree accuracy). We contrast results during clear-sky and partly cloudy conditions documenting sun-tracking performance at manufacturer specified accuracies for active tracking (0.02 degrees) and highlight accuracies achieved during passive tracking i.e. periods with less than 300 W m-2 direct radiation. Furthermore we detail limitations to tracking surveillance during overcast conditions and periods of partial solar limb coverage by clouds.

Tracking the Evolution of Infrastructure Systems and Mass Responses Using Publically Available Data

PubMed Central

Guan, Xiangyang; Chen, Cynthia; Work, Dan

2016-01-01

Networks can evolve even on a short-term basis. This phenomenon is well understood by network scientists, but receive little attention in empirical literature involving real-world networks. On one hand, this is due to the deceitfully fixed topology of some networks such as many physical infrastructures, whose evolution is often deemed unlikely to occur in short term; on the other hand, the lack of data prohibits scientists from studying subjects such as social networks that seem likely to evolve on a short-term basis. We show that both networks—the infrastructure network and social network—are able to demonstrate evolutionary dynamics at the system level even in the short-term, characterized by shifting between different phases as predicted in network science. We develop a methodology of tracking the evolutionary dynamics of the two networks by incorporating flows and the microstructure of networks such as motifs. This approach is applied to the human interaction network and two transportation networks (subway and taxi) in the context of Hurricane Sandy, using publically available Twitter data and transportation data. Our result shows that significant changes in the system-level structure of networks can be detected on a continuous basis. This result provides a promising channel for real-time tracking in the future. PMID:27907061

Domain-General Brain Regions Do Not Track Linguistic Input as Closely as Language-Selective Regions

PubMed Central

Fedorenko, Evelina

2017-01-01

Language comprehension engages a cortical network of left frontal and temporal regions. Activity in this network is language-selective, showing virtually no modulation by nonlinguistic tasks. In addition, language comprehension engages a second network consisting of bilateral frontal, parietal, cingulate, and insular regions. Activity in this “multiple demand” (MD) network scales with comprehension difficulty, but also with cognitive effort across a wide range of nonlinguistic tasks in a domain-general fashion. Given the functional dissociation between the language and MD networks, their respective contributions to comprehension are likely distinct, yet such differences remain elusive. Prior neuroimaging studies have suggested that activity in each network covaries with some linguistic features that, behaviorally, influence on-line processing and comprehension. This sensitivity of the language and MD networks to local input characteristics has often been interpreted, implicitly or explicitly, as evidence that both networks track linguistic input closely, and in a manner consistent across individuals. Here, we used fMRI to directly test this assumption by comparing the BOLD signal time courses in each network across different people (n = 45, men and women) listening to the same story. Language network activity showed fewer individual differences, indicative of closer input tracking, whereas MD network activity was more idiosyncratic and, moreover, showed lower reliability within an individual across repetitions of a story. These findings constrain cognitive models of language comprehension by suggesting a novel distinction between the processes implemented in the language and MD networks. SIGNIFICANCE STATEMENT Language comprehension recruits both language-specific mechanisms and domain-general mechanisms that are engaged in many cognitive processes. In the human cortex, language-selective mechanisms are implemented in the left-lateralized “core language network”, whereas domain-general mechanisms are implemented in the bilateral “multiple demand” (MD) network. Here, we report the first direct comparison of the respective contributions of these networks to naturalistic story comprehension. Using a novel combination of neuroimaging approaches we find that MD regions track stories less closely than language regions. This finding constrains the possible contributions of the MD network to comprehension, contrasts with accounts positing that this network has continuous access to linguistic input, and suggests a new typology of comprehension processes based on their extent of input tracking. PMID:28871034

Toward Collaboration Sensing

ERIC Educational Resources Information Center

Schneider, Bertrand; Pea, Roy

2014-01-01

We describe preliminary applications of network analysis techniques to eye-tracking data collected during a collaborative learning activity. This paper makes three contributions: first, we visualize collaborative eye-tracking data as networks, where the nodes of the graph represent fixations and edges represent saccades. We found that those…

Neural network based satellite tracking for deep space applications

NASA Technical Reports Server (NTRS)

Amoozegar, F.; Ruggier, C.

2003-01-01

The objective of this paper is to provide a survey of neural network trends as applied to the tracking of spacecrafts in deep space at Ka-band under various weather conditions and examine the trade-off between tracing accuracy and communication link performance.

Networking. New Opportunities for Partnering, CAUSE94. Track IV.

ERIC Educational Resources Information Center

CAUSE, Boulder, CO.

Seven papers are presented from the 1994 CAUSE conference track on networking and information sharing among higher education institutions. The papers include: (1) "Integrated Statewide Infrastructure of Learning Technologies," focusing on the University of Wisconsin System (Lee Alley); (2) "Designing and Implementing a Network…

Assessing Performance Tradeoffs in Undersea Distributed Sensor Networks

DTIC Science & Technology

2006-09-01

time. We refer to this process as track - before - detect (see [5] for a description), since the final determination of a target presence is not made until...expressions for probability of successful search and probability of false search for modeling the track - before - detect process. We then describe a numerical...random manner (randomly sampled from a uniform distribution). II. SENSOR NETWORK PERFORMANCE MODELS We model the process of track - before - detect by

Track inspection planning and risk measurement analysis.

DOT National Transportation Integrated Search

2014-11-01

This project models track inspection operations on a railroad network and discusses how the inspection results can : be used to measure the risk of failure on the tracks. In particular, the inspection times of the tracks, inspection frequency of the ...

Track classification within wireless sensor network

NASA Astrophysics Data System (ADS)

Doumerc, Robin; Pannetier, Benjamin; Moras, Julien; Dezert, Jean; Canevet, Loic

2017-05-01

In this paper, we present our study on track classification by taking into account environmental information and target estimated states. The tracker uses several motion model adapted to different target dynamics (pedestrian, ground vehicle and SUAV, i.e. small unmanned aerial vehicle) and works in centralized architecture. The main idea is to explore both: classification given by heterogeneous sensors and classification obtained with our fusion module. The fusion module, presented in his paper, provides a class on each track according to track location, velocity and associated uncertainty. To model the likelihood on each class, a fuzzy approach is used considering constraints on target capability to move in the environment. Then the evidential reasoning approach based on Dempster-Shafer Theory (DST) is used to perform a time integration of this classifier output. The fusion rules are tested and compared on real data obtained with our wireless sensor network.In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of this system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

Network exploitation using WAMI tracks

NASA Astrophysics Data System (ADS)

Rimey, Ray; Record, Jim; Keefe, Dan; Kennedy, Levi; Cramer, Chris

2011-06-01

Creating and exploiting network models from wide area motion imagery (WAMI) is an important task for intelligence analysis. Tracks of entities observed moving in the WAMI sensor data are extracted, then large numbers of tracks are studied over long time intervals to determine specific locations that are visited (e.g., buildings in an urban environment), what locations are related to other locations, and the function of each location. This paper describes several parts of the network detection/exploitation problem, and summarizes a solution technique for each: (a) Detecting nodes; (b) Detecting links between known nodes; (c) Node attributes to characterize a node; (d) Link attributes to characterize each link; (e) Link structure inferred from node attributes and vice versa; and (f) Decomposing a detected network into smaller networks. Experimental results are presented for each solution technique, and those are used to discuss issues for each problem part and its solution technique.

Tracking state deployments of commercial vehicle information systems and networks : national report

DOT National Transportation Integrated Search

1998-03-31

The ITS Joint Program Office (ITS/JPO) of the USDOT has begun tracking progress by state governments in the deployment of Commercial Vehicle Information Systems and Networks (CVISN) in all 50 states through the year 2005. FHWAs goal is to have bet...

Global tracking of space debris via CPHD and consensus

NASA Astrophysics Data System (ADS)

Wei, Baishen; Nener, Brett; Liu, Weifeng; Ma, Liang

2017-05-01

Space debris tracking is of great importance for safe operation of spacecraft. This paper presents an algorithm that achieves global tracking of space debris with a multi-sensor network. The sensor network has unknown and possibly time-varying topology. A consensus algorithm is used to effectively counteract the effects of data incest. Gaussian Mixture-Cardinalized Probability Hypothesis Density (GM-CPHD) filtering is used to estimate the state of the space debris. As an example of the method, 45 clusters of sensors are used to achieve global tracking. The performance of the proposed approach is demonstrated by simulation experiments.

Studies of pointing, acquisition, and tracking of agile optical wireless transceivers for free-space optical communication networks

NASA Astrophysics Data System (ADS)

Ho, Tzung-Hsien; Trisno, Sugianto; Smolyaninov, Igor I.; Milner, Stuart D.; Davis, Christopher C.

2004-02-01

Free space, dynamic, optical wireless communications will require topology control for optimization of network performance. Such networks may need to be configured for bi- or multiple-connectedness, reliability and quality-of-service. Topology control involves the introduction of new links and/or nodes into the network to achieve such performance objectives through autonomous reconfiguration as well as precise pointing, acquisition, tracking, and steering of laser beams. Reconfiguration may be required because of link degradation resulting from obscuration or node loss. As a result, the optical transceivers may need to be re-directed to new or existing nodes within the network and tracked on moving nodes. The redirection of transceivers may require operation over a whole sphere, so that small-angle beam steering techniques cannot be applied. In this context, we are studying the performance of optical wireless links using lightweight, bi-static transceivers mounted on high-performance stepping motor driven stages. These motors provide an angular resolution of 0.00072 degree at up to 80,000 steps per second. This paper focuses on the performance characteristics of these agile transceivers for pointing, acquisition, and tracking (PAT), including the influence of acceleration/deceleration time, motor angular speed, and angular re-adjustment, on latency and packet loss in small free space optical (FSO) wireless test networks.

Data to Action: Using Environmental Public Health Tracking to Inform Decision Making

PubMed Central

Qualters, Judith R; Strosnider, Heather M; Bell, Rosalyn

2017-01-01

Context Public health surveillance includes dissemination of data and information to those who need it to take action to prevent or control disease. The concept of data to action is explicit in the mission of the Centers for Disease Control and Prevention’s (CDC) National Environmental Public Health Tracking Program (Tracking Program). CDC has built a National Environmental Public Health Tracking Network (Tracking Network) to integrate health and environmental data to drive public health action (PHA) to improve communities’ health. Objective To assess the utility of the Tracking Program and its Network in environmental public health practice and policy-making. Design We analyzed information on how Tracking has been used to drive PHAs within funded states and cities (grantees). Two case studies illustrate such use. Setting Analyses included all grantees funded between 2005 and 2013. Participants The number of grantees varied from 17 for 2006–2008 to 24 for 2010–2013. Main Outcome Measures We categorized each PHA reported to determine how grantees became involved, their role, the problems addressed, and the overall action. Results Tracking grantees reported 178 PHAs from 2006–2013. The most common overall action was “provided information in response to concern” (n=42) followed by “improved a public health program, intervention, or response plan” (n=35). Tracking’s role was most often to enhance surveillance (24%) or to analyze data (23%). In 47% of PHAs, the underlying problem was a concern about possible elevated rates of a health outcome, a potential exposure, or a potential association between a hazard and health. PHAs were started by a request for assistance (48%), in response to an emergency (8%), and though routine work by Tracking programs (43%). Conclusion Our review shows that the data, expertise, technical infrastructure, and other resources of the Tracking Program and its Network are driving state and local PHAs. PMID:25621441

76 FR 41262 - Notice of Intent To Award Affordable Care Act (ACA) Funding, EH11-1103

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-13

... Intent To Award Affordable Care Act (ACA) Funding, EH11-1103 Notice of Intent to award Affordable Care... opportunity EH11-1103, ``National Environmental Public Health Tracking Program-Network Implementation... under funding opportunity EH11-1103, ``National Environmental Public Health Tracking Program-Network...

Transforming War Fighting through the Use of Service Based Architecture (SBA) Technology

DTIC Science & Technology

2006-05-04

near-real-time video & telemetry to users on network using standard web-based protocols – Provides web-based access to archived video files MTI...Target Tracks Service Capabilities – Disseminates near-real-time MTI and Target Tracks to users on network based on consumer specified geographic...filter IBS SIGINT Service Capabilities – Disseminates near-real-time IBS SIGINT data to users on network based on consumer specified geographic filter

Neural network fusion capabilities for efficient implementation of tracking algorithms

NASA Astrophysics Data System (ADS)

Sundareshan, Malur K.; Amoozegar, Farid

1996-05-01

The ability to efficiently fuse information of different forms for facilitating intelligent decision-making is one of the major capabilities of trained multilayer neural networks that is being recognized int eh recent times. While development of innovative adaptive control algorithms for nonlinear dynamical plants which attempt to exploit these capabilities seems to be more popular, a corresponding development of nonlinear estimation algorithms using these approaches, particularly for application in target surveillance and guidance operations, has not received similar attention. In this paper we describe the capabilities and functionality of neural network algorithms for data fusion and implementation of nonlinear tracking filters. For a discussion of details and for serving as a vehicle for quantitative performance evaluations, the illustrative case of estimating the position and velocity of surveillance targets is considered. Efficient target tracking algorithms that can utilize data from a host of sensing modalities and are capable of reliably tracking even uncooperative targets executing fast and complex maneuvers are of interest in a number of applications. The primary motivation for employing neural networks in these applications comes form the efficiency with which more features extracted from different sensor measurements can be utilized as inputs for estimating target maneuvers. Such an approach results in an overall nonlinear tracking filter which has several advantages over the popular efforts at designing nonlinear estimation algorithms for tracking applications, the principle one being the reduction of mathematical and computational complexities. A system architecture that efficiently integrates the processing capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described in this paper.

Use of artificial neural networks on optical track width measurements.

PubMed

Smith, Richard J; See, Chung W; Somekh, Mike G; Yacoot, Andrew

2007-08-01

We have demonstrated recently that, by using an ultrastable optical interferometer together with artificial neural networks (ANNs), track widths down to 60 nm can be measured with a 0.3 NA objective lens. We investigate the effective conditions for training ANNs. Experimental results will be used to show the characteristics of the training samples and the data format of the ANN inputs required to produce suitably trained ANNs. Results obtained with networks measuring double tracks, and classifying different structures, will be presented to illustrate the capability of the technique. We include a discussion on expansion of the application areas of the system, allowing it to be used as a general purpose instrument.

Use of artificial neural networks on optical track width measurements

NASA Astrophysics Data System (ADS)

Smith, Richard J.; See, Chung W.; Somekh, Mike G.; Yacoot, Andrew

2007-08-01

We have demonstrated recently that, by using an ultrastable optical interferometer together with artificial neural networks (ANNs), track widths down to 60 nm can be measured with a 0.3 NA objective lens. We investigate the effective conditions for training ANNs. Experimental results will be used to show the characteristics of the training samples and the data format of the ANN inputs required to produce suitably trained ANNs. Results obtained with networks measuring double tracks, and classifying different structures, will be presented to illustrate the capability of the technique. We include a discussion on expansion of the application areas of the system, allowing it to be used as a general purpose instrument.

Tracking and data system support for the Viking 1975 mission to Mars. Volume 3: Planetary operations

NASA Technical Reports Server (NTRS)

Mudgway, D. J.

1977-01-01

The support provided by the Deep Space Network to the 1975 Viking Mission from the first landing on Mars July 1976 to the end of the Prime Mission on November 15, 1976 is described and evaluated. Tracking and data acquisition support required the continuous operation of a worldwide network of tracking stations with 64-meter and 26-meter diameter antennas, together with a global communications system for the transfer of commands, telemetry, and radio metric data between the stations and the Network Operations Control Center in Pasadena, California. Performance of the deep-space communications links between Earth and Mars, and innovative new management techniques for operations and data handling are included.

NASA directory of observation station locations, volume 1

NASA Technical Reports Server (NTRS)

1973-01-01

Geodetic information for NASA tracking stations and for observation stations cooperating in NASA geodetic satellite programs is presented. A Geodetic Data Sheet is provided for each station, giving the position of the station and describing briefly how it was established. Geodetic positions and geocentric coordinates of these stations are tabulated on local or major geodetic datums and on selected world geodetic systems. The principal tracking facilities used by NASA, including the Spaceflight Tracking and Data Network, the Deep Space Network, and several large radio telescopes are discussed. Positions of these facilities are tabulated on their local or national datums, the Mercury Spheroid 1960, the Modified Mercury Datum 1968, and the Spaceflight Tracking and Data Network System. Observation stations in the NASA Geodetic Satellites Program are included along with stations participating in the National Geodetic Satellite Program. Positions of these facilities are given on local or preferred major datums, and on the Modified Mercury Datum 1968.

Region stability analysis and tracking control of memristive recurrent neural network.

PubMed

Bao, Gang; Zeng, Zhigang; Shen, Yanjun

2018-02-01

Memristor is firstly postulated by Leon Chua and realized by Hewlett-Packard (HP) laboratory. Research results show that memristor can be used to simulate the synapses of neurons. This paper presents a class of recurrent neural network with HP memristors. Firstly, it shows that memristive recurrent neural network has more compound dynamics than the traditional recurrent neural network by simulations. Then it derives that n dimensional memristive recurrent neural network is composed of [Formula: see text] sub neural networks which do not have a common equilibrium point. By designing the tracking controller, it can make memristive neural network being convergent to the desired sub neural network. At last, two numerical examples are given to verify the validity of our result. Copyright © 2017 Elsevier Ltd. All rights reserved.

Tracking and data system support for the Viking 1975 mission to Mars. Volume 2: Launch through landing of Viking 1

NASA Technical Reports Server (NTRS)

Mudgway, D. J.; Traxler, M. R.

1977-01-01

Problems inherent in the deployment and management of a worldwide tracking and data acquisition network to support the two Viking Orbiters and two Viking Landers simultaneously over 320 million kilometers (200 million miles) of deep space are discussed. Activities described include tracking coverage of the launch phase, the deep space operations during the long cruise phase that occupied approximately 11 months, and the implementation of the a vast worldwide network of tracking sttions and global communications systems. The performance of the personnel, hardware, and software involved in this vast undertaking are evaluated.

Walking- and cycling track networks in Norwegian cities : cost-benefit analyses including health effects and external costs of road traffic : summary

DOT National Transportation Integrated Search

2002-04-01

Cost- benefit analyses of walking- and cycling track net-works in three Norwegian cities are presented in this study. A project group working with a National Cycling Strategy in Norway initialised the study. Motivation for starting the study is the P...

Multi-target Detection, Tracking, and Data Association on Road Networks Using Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Barkley, Brett E.

A cooperative detection and tracking algorithm for multiple targets constrained to a road network is presented for fixed-wing Unmanned Air Vehicles (UAVs) with a finite field of view. Road networks of interest are formed into graphs with nodes that indicate the target likelihood ratio (before detection) and position probability (after detection). A Bayesian likelihood ratio tracker recursively assimilates target observations until the cumulative observations at a particular location pass a detection criterion. At this point, a target is considered detected and a position probability is generated for the target on the graph. Data association is subsequently used to route future measurements to update the likelihood ratio tracker (for undetected target) or to update a position probability (a previously detected target). Three strategies for motion planning of UAVs are proposed to balance searching for new targets with tracking known targets for a variety of scenarios. Performance was tested in Monte Carlo simulations for a variety of mission parameters, including tracking on road networks with varying complexity and using UAVs at various altitudes.

Moving target tracking through distributed clustering in directional sensor networks.

PubMed

Enayet, Asma; Razzaque, Md Abdur; Hassan, Mohammad Mehedi; Almogren, Ahmad; Alamri, Atif

2014-12-18

The problem of moving target tracking in directional sensor networks (DSNs) introduces new research challenges, including optimal selection of sensing and communication sectors of the directional sensor nodes, determination of the precise location of the target and an energy-efficient data collection mechanism. Existing solutions allow individual sensor nodes to detect the target's location through collaboration among neighboring nodes, where most of the sensors are activated and communicate with the sink. Therefore, they incur much overhead, loss of energy and reduced target tracking accuracy. In this paper, we have proposed a clustering algorithm, where distributed cluster heads coordinate their member nodes in optimizing the active sensing and communication directions of the nodes, precisely determining the target location by aggregating reported sensing data from multiple nodes and transferring the resultant location information to the sink. Thus, the proposed target tracking mechanism minimizes the sensing redundancy and maximizes the number of sleeping nodes in the network. We have also investigated the dynamic approach of activating sleeping nodes on-demand so that the moving target tracking accuracy can be enhanced while maximizing the network lifetime. We have carried out our extensive simulations in ns-3, and the results show that the proposed mechanism achieves higher performance compared to the state-of-the-art works.

Moving Target Tracking through Distributed Clustering in Directional Sensor Networks

PubMed Central

Enayet, Asma; Razzaque, Md. Abdur; Hassan, Mohammad Mehedi; Almogren, Ahmad; Alamri, Atif

2014-01-01

The problem of moving target tracking in directional sensor networks (DSNs) introduces new research challenges, including optimal selection of sensing and communication sectors of the directional sensor nodes, determination of the precise location of the target and an energy-efficient data collection mechanism. Existing solutions allow individual sensor nodes to detect the target's location through collaboration among neighboring nodes, where most of the sensors are activated and communicate with the sink. Therefore, they incur much overhead, loss of energy and reduced target tracking accuracy. In this paper, we have proposed a clustering algorithm, where distributed cluster heads coordinate their member nodes in optimizing the active sensing and communication directions of the nodes, precisely determining the target location by aggregating reported sensing data from multiple nodes and transferring the resultant location information to the sink. Thus, the proposed target tracking mechanism minimizes the sensing redundancy and maximizes the number of sleeping nodes in the network. We have also investigated the dynamic approach of activating sleeping nodes on-demand so that the moving target tracking accuracy can be enhanced while maximizing the network lifetime. We have carried out our extensive simulations in ns-3, and the results show that the proposed mechanism achieves higher performance compared to the state-of-the-art works. PMID:25529205

An Expert System And Simulation Approach For Sensor Management & Control In A Distributed Surveillance Network

NASA Astrophysics Data System (ADS)

Leon, Barbara D.; Heller, Paul R.

1987-05-01

A surveillance network is a group of multiplatform sensors cooperating to improve network performance. Network control is distributed as a measure to decrease vulnerability to enemy threat. The network may contain diverse sensor types such as radar, ESM (Electronic Support Measures), IRST (Infrared search and track) and E-0 (Electro-Optical). Each platform may contain a single sensor or suite of sensors. In a surveillance network it is desirable to control sensors to make the overall system more effective. This problem has come to be known as sensor management and control (SM&C). Two major facets of network performance are surveillance and survivability. In a netted environment, surveillance can be enhanced if information from all sensors is combined and sensor operating conditions are controlled to provide a synergistic effect. In contrast, when survivability is the main concern for the network, the best operating status for all sensors would be passive or off. Of course, improving survivability tends to degrade surveillance. Hence, the objective of SM&C is to optimize surveillance and survivability of the network. Too voluminous data of various formats and the quick response time are two characteristics of this problem which make it an ideal application for Artificial Intelligence. A solution to the SM&C problem, presented as a computer simulation, will be presented in this paper. The simulation is a hybrid production written in LISP and FORTRAN. It combines the latest conventional computer programming methods with Artificial Intelligence techniques to produce a flexible state-of-the-art tool to evaluate network performance. The event-driven simulation contains environment models coupled with an expert system. These environment models include sensor (track-while-scan and agile beam) and target models, local tracking, and system tracking. These models are used to generate the environment for the sensor management and control expert system. The expert system, driven by a forward chaining inference engine, makes decisions based on the global database. The global database contains current track and sensor information supplied by the simulation. At present, the rule base emphasizes the surveillance features with rules grouped into three main categories: maintenance and enhancing track on prioritized targets; filling coverage holes and countering jamming; and evaluating sensor status. The paper will describe the architecture used for the expert system and the reasons for selecting the chosen methods. The SM&C simulation produces a graphical representation of sensors and their associated tracks such that the benefits of the sensor management and control expert system are evident. Jammer locations are also part of the display. The paper will describe results from several scenarios that best illustrate the sensor management and control concepts.

The deep space network, volume 6

NASA Technical Reports Server (NTRS)

1971-01-01

Progress on Deep Space Network (DSN) supporting research and technology is presented, together with advanced development and engineering, implementation, and DSN operations of flight projects. The DSN is described. Interplanetary and planetary flight projects and radio science experiments are discussed. Tracking and navigational accuracy analysis, communications systems and elements research, and supporting research are considered. Development of the ground communications and deep space instrumentation facilities is also presented. Network allocation schedules and angle tracking and test development are included.

Advanced algorithms for distributed fusion

NASA Astrophysics Data System (ADS)

Gelfand, A.; Smith, C.; Colony, M.; Bowman, C.; Pei, R.; Huynh, T.; Brown, C.

2008-03-01

The US Military has been undergoing a radical transition from a traditional "platform-centric" force to one capable of performing in a "Network-Centric" environment. This transformation will place all of the data needed to efficiently meet tactical and strategic goals at the warfighter's fingertips. With access to this information, the challenge of fusing data from across the batttlespace into an operational picture for real-time Situational Awareness emerges. In such an environment, centralized fusion approaches will have limited application due to the constraints of real-time communications networks and computational resources. To overcome these limitations, we are developing a formalized architecture for fusion and track adjudication that allows the distribution of fusion processes over a dynamically created and managed information network. This network will support the incorporation and utilization of low level tracking information within the Army Distributed Common Ground System (DCGS-A) or Future Combat System (FCS). The framework is based on Bowman's Dual Node Network (DNN) architecture that utilizes a distributed network of interlaced fusion and track adjudication nodes to build and maintain a globally consistent picture across all assets.

Track-weighted functional connectivity (TW-FC): a tool for characterizing the structural-functional connections in the brain.

PubMed

Calamante, Fernando; Masterton, Richard A J; Tournier, Jacques-Donald; Smith, Robert E; Willats, Lisa; Raffelt, David; Connelly, Alan

2013-04-15

MRI provides a powerful tool for studying the functional and structural connections in the brain non-invasively. The technique of functional connectivity (FC) exploits the intrinsic temporal correlations of slow spontaneous signal fluctuations to characterise brain functional networks. In addition, diffusion MRI fibre-tracking can be used to study the white matter structural connections. In recent years, there has been considerable interest in combining these two techniques to provide an overall structural-functional description of the brain. In this work we applied the recently proposed super-resolution track-weighted imaging (TWI) methodology to demonstrate how whole-brain fibre-tracking data can be combined with FC data to generate a track-weighted (TW) FC map of FC networks. The method was applied to data from 8 healthy volunteers, and illustrated with (i) FC networks obtained using a seeded connectivity-based analysis (seeding in the precuneus/posterior cingulate cortex, PCC, known to be part of the default mode network), and (ii) with FC networks generated using independent component analysis (in particular, the default mode, attention, visual, and sensory-motor networks). TW-FC maps showed high intensity in white matter structures connecting the nodes of the FC networks. For example, the cingulum bundles show the strongest TW-FC values in the PCC seeded-based analysis, due to their major role in the connection between medial frontal cortex and precuneus/posterior cingulate cortex; similarly the superior longitudinal fasciculus was well represented in the attention network, the optic radiations in the visual network, and the corticospinal tract and corpus callosum in the sensory-motor network. The TW-FC maps highlight the white matter connections associated with a given FC network, and their intensity in a given voxel reflects the functional connectivity of the part of the nodes of the network linked by the structural connections traversing that voxel. They therefore contain a different (and novel) image contrast from that of the images used to generate them. The results shown in this study illustrate the potential of the TW-FC approach for the fusion of structural and functional data into a single quantitative image. This technique could therefore have important applications in neuroscience and neurology, such as for voxel-based comparison studies. Copyright © 2012 Elsevier Inc. All rights reserved.

The deep space network

NASA Technical Reports Server (NTRS)

1977-01-01

Presented is Deep Space Network (DSN) progress in flight project support, tracking and data acquisition (TDA) research and technology, network engineering, hardware and software implementation, and operations.

The deep space network

NASA Technical Reports Server (NTRS)

1975-01-01

Summaries are given of Deep Space Network progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations.

Neural network robust tracking control with adaptive critic framework for uncertain nonlinear systems.

PubMed

Wang, Ding; Liu, Derong; Zhang, Yun; Li, Hongyi

2018-01-01

In this paper, we aim to tackle the neural robust tracking control problem for a class of nonlinear systems using the adaptive critic technique. The main contribution is that a neural-network-based robust tracking control scheme is established for nonlinear systems involving matched uncertainties. The augmented system considering the tracking error and the reference trajectory is formulated and then addressed under adaptive critic optimal control formulation, where the initial stabilizing controller is not needed. The approximate control law is derived via solving the Hamilton-Jacobi-Bellman equation related to the nominal augmented system, followed by closed-loop stability analysis. The robust tracking control performance is guaranteed theoretically via Lyapunov approach and also verified through simulation illustration. Copyright © 2017 Elsevier Ltd. All rights reserved.

Nonlinear Motion Tracking by Deep Learning Architecture

NASA Astrophysics Data System (ADS)

Verma, Arnav; Samaiya, Devesh; Gupta, Karunesh K.

2018-03-01

In the world of Artificial Intelligence, object motion tracking is one of the major problems. The extensive research is being carried out to track people in crowd. This paper presents a unique technique for nonlinear motion tracking in the absence of prior knowledge of nature of nonlinear path that the object being tracked may follow. We achieve this by first obtaining the centroid of the object and then using the centroid as the current example for a recurrent neural network trained using real-time recurrent learning. We have tweaked the standard algorithm slightly and have accumulated the gradient for few previous iterations instead of using just the current iteration as is the norm. We show that for a single object, such a recurrent neural network is highly capable of approximating the nonlinearity of its path.

Effects of measurement unobservability on neural extended Kalman filter tracking

NASA Astrophysics Data System (ADS)

Stubberud, Stephen C.; Kramer, Kathleen A.

2009-05-01

An important component of tracking fusion systems is the ability to fuse various sensors into a coherent picture of the scene. When multiple sensor systems are being used in an operational setting, the types of data vary. A significant but often overlooked concern of multiple sensors is the incorporation of measurements that are unobservable. An unobservable measurement is one that may provide information about the state, but cannot recreate a full target state. A line of bearing measurement, for example, cannot provide complete position information. Often, such measurements come from passive sensors such as a passive sonar array or an electronic surveillance measure (ESM) system. Unobservable measurements will, over time, result in the measurement uncertainty to grow without bound. While some tracking implementations have triggers to protect against the detrimental effects, many maneuver tracking algorithms avoid discussing this implementation issue. One maneuver tracking technique is the neural extended Kalman filter (NEKF). The NEKF is an adaptive estimation algorithm that estimates the target track as it trains a neural network on line to reduce the error between the a priori target motion model and the actual target dynamics. The weights of neural network are trained in a similar method to the state estimation/parameter estimation Kalman filter techniques. The NEKF has been shown to improve target tracking accuracy through maneuvers and has been use to predict target behavior using the new model that consists of the a priori model and the neural network. The key to the on-line adaptation of the NEKF is the fact that the neural network is trained using the same residuals as the Kalman filter for the tracker. The neural network weights are treated as augmented states to the target track. Through the state-coupling function, the weights are coupled to the target states. Thus, if the measurements cause the states of the target track to be unobservable, then the weights of the neural network have unobservable modes as well. In recent analysis, the NEKF was shown to have a significantly larger growth in the eigenvalues of the error covariance matrix than the standard EKF tracker when the measurements were purely bearings-only. This caused detrimental effects to the ability of the NEKF to model the target dynamics. In this work, the analysis is expanded to determine the detrimental effects of bearings-only measurements of various uncertainties on the performance of the NEKF when these unobservable measurements are interlaced with completely observable measurements. This analysis provides the ability to put implementation limitations on the NEKF when bearings-only sensors are present.

Scoring sensor observations to facilitate the exchange of space surveillance data

NASA Astrophysics Data System (ADS)

Weigel, M.; Fiedler, H.; Schildknecht, T.

2017-08-01

In this paper, a scoring metric for space surveillance sensor observations is introduced. A scoring metric allows for direct comparison of data quantity and data quality, and makes transparent the effort made by different sensor operators. The concept might be applied to various sensor types like tracking and surveillance radar, active optical laser tracking, or passive optical telescopes as well as combinations of different measurement types. For each measurement type, a polynomial least squares fit is performed on the measurement values contained in the track. The track score is the average sum over the polynomial coefficients uncertainties and scaled by reference measurement accuracy. Based on the newly developed scoring metric, an accounting model and a rating model are introduced. Both models facilitate the exchange of observation data within a network of space surveillance sensors operators. In this paper, optical observations are taken as an example for analysis purposes, but both models can also be utilized for any other type of observations. The rating model has the capability to distinguish between network participants with major and minor data contribution to the network. The level of sanction on data reception is defined by the participants themselves enabling a high flexibility. The more elaborated accounting model translates the track score to credit points earned for data provision and spend for data reception. In this model, data reception is automatically limited for participants with low contribution to the network. The introduced method for observation scoring is first applied for transparent data exchange within the Small Aperture Robotic Telescope Network (SMARTnet). Therefore a detailed mathematical description is presented for line of sight measurements from optical telescopes, as well as numerical simulations for different network setups.

Multi-Topic Tracking Model for dynamic social network

NASA Astrophysics Data System (ADS)

Li, Yuhua; Liu, Changzheng; Zhao, Ming; Li, Ruixuan; Xiao, Hailing; Wang, Kai; Zhang, Jun

2016-07-01

The topic tracking problem has attracted much attention in the last decades. However, existing approaches rarely consider network structures and textual topics together. In this paper, we propose a novel statistical model based on dynamic bayesian network, namely Multi-Topic Tracking Model for Dynamic Social Network (MTTD). It takes influence phenomenon, selection phenomenon, document generative process and the evolution of textual topics into account. Specifically, in our MTTD model, Gibbs Random Field is defined to model the influence of historical status of users in the network and the interdependency between them in order to consider the influence phenomenon. To address the selection phenomenon, a stochastic block model is used to model the link generation process based on the users' interests to topics. Probabilistic Latent Semantic Analysis (PLSA) is used to describe the document generative process according to the users' interests. Finally, the dependence on the historical topic status is also considered to ensure the continuity of the topic itself in topic evolution model. Expectation Maximization (EM) algorithm is utilized to estimate parameters in the proposed MTTD model. Empirical experiments on real datasets show that the MTTD model performs better than Popular Event Tracking (PET) and Dynamic Topic Model (DTM) in generalization performance, topic interpretability performance, topic content evolution and topic popularity evolution performance.

An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier architecture.

PubMed

Doulamis, A; Doulamis, N; Ntalianis, K; Kollias, S

2003-01-01

In this paper, an unsupervised video object (VO) segmentation and tracking algorithm is proposed based on an adaptable neural-network architecture. The proposed scheme comprises: 1) a VO tracking module and 2) an initial VO estimation module. Object tracking is handled as a classification problem and implemented through an adaptive network classifier, which provides better results compared to conventional motion-based tracking algorithms. Network adaptation is accomplished through an efficient and cost effective weight updating algorithm, providing a minimum degradation of the previous network knowledge and taking into account the current content conditions. A retraining set is constructed and used for this purpose based on initial VO estimation results. Two different scenarios are investigated. The first concerns extraction of human entities in video conferencing applications, while the second exploits depth information to identify generic VOs in stereoscopic video sequences. Human face/ body detection based on Gaussian distributions is accomplished in the first scenario, while segmentation fusion is obtained using color and depth information in the second scenario. A decision mechanism is also incorporated to detect time instances for weight updating. Experimental results and comparisons indicate the good performance of the proposed scheme even in sequences with complicated content (object bending, occlusion).

Distributed Tracking in Distributed Sensor Networks

DTIC Science & Technology

1988-05-26

Glocal Track 6-17 6-12: Case II: Initial Glocal Track 6-18 6-13: Local Tracking Results with Multiple Model Approach 6-19 6-14: Model Probability History...3480.0- 2290.0e iee. onee -5800 -4600.8 -3400.8 -2208.8 -1886 X (Mi) Figure 6-11: Case 1: Initial Glocal Track 6-17 460. 420. 38 . 3488.9 1st 3498.9

The New Space Network: the Tracking and Data Relay Satellite System

NASA Technical Reports Server (NTRS)

Froehlich, W.

1986-01-01

When the Tracking and Data Relay Satellite System (TDRSS)is completed, the system, together with its various NASA support elements will be known simply as the Space Networks. It will substantially increase information exchanges between low-orbiting spacecraft and the ground. The structural design, functions, earth-based links, and present and future use are discussed.

GeoTrack: bio-inspired global video tracking by networks of unmanned aircraft systems

NASA Astrophysics Data System (ADS)

Barooah, Prabir; Collins, Gaemus E.; Hespanha, João P.

2009-05-01

Research from the Institute for Collaborative Biotechnologies (ICB) at the University of California at Santa Barbara (UCSB) has identified swarming algorithms used by flocks of birds and schools of fish that enable these animals to move in tight formation and cooperatively track prey with minimal estimation errors, while relying solely on local communication between the animals. This paper describes ongoing work by UCSB, the University of Florida (UF), and the Toyon Research Corporation on the utilization of these algorithms to dramatically improve the capabilities of small unmanned aircraft systems (UAS) to cooperatively locate and track ground targets. Our goal is to construct an electronic system, called GeoTrack, through which a network of hand-launched UAS use dedicated on-board processors to perform multi-sensor data fusion. The nominal sensors employed by the system will EO/IR video cameras on the UAS. When GMTI or other wide-area sensors are available, as in a layered sensing architecture, data from the standoff sensors will also be fused into the GeoTrack system. The output of the system will be position and orientation information on stationary or mobile targets in a global geo-stationary coordinate system. The design of the GeoTrack system requires significant advances beyond the current state-of-the-art in distributed control for a swarm of UAS to accomplish autonomous coordinated tracking; target geo-location using distributed sensor fusion by a network of UAS, communicating over an unreliable channel; and unsupervised real-time image-plane video tracking in low-powered computing platforms.

New approaches for tracking earth orbiters using modified GPS ground receivers

NASA Technical Reports Server (NTRS)

Lichten, S. M.; Young, L. E.; Nandi, S.; Haines, B. J.; Dunn, C. E.; Edwards, C. D.

1993-01-01

A Global Positioning System (GPS) flight receiver provides a means to precisely determine orbits for satellites in low to moderate altitude orbits. Above a 5000-km altitude, however, relatively few GPS satellites are visible. New approaches to orbit determination for satellites at higher altitudes could reduce DSN antenna time needed to provide navigation and orbit determination support to future missions. Modification of GPS ground receivers enables a beacon from the orbiter to be tracked simultaneously with GPS data. The orbit accuracy expected from this GPS-like tracking (GLT) technique is expected to be in the range of a few meters or better for altitudes up to 100,000 km with a global ground network. For geosynchronous satellites, however, there are unique challenges due to geometrical limitations and to the lack of strong dynamical signature in tracking data. We examine two approaches for tracking the Tracking and Data Relay Satellite System (TDRSS) geostationary orbiters. One uses GLT with a global network; the other relies on a small 'connected element' ground network with a distributed clock for short-baseline differential carrier phase (SB Delta Phi). We describe an experiment planned for late 1993, which will combine aspects of both GLT and SB Delta Phi, to demonstrate a new approach for tracking the Tracking and Data Relay Satellites (TDRSs) that offers a number of operationally convenient and attractive features. The TDRS demonstration will be in effect a proof-of-concept experiment for a new approach to tracking spacecraft which could be applied more generally to deep-space as well as near-Earth regimes.

SCIGN; new Southern California GPS network advances the study of earthquakes

USGS Publications Warehouse

Hudnut, Ken; King, Nancy

2001-01-01

Southern California is a giant jigsaw puzzle, and scientists are now using GPS satellites to track the pieces. These puzzle pieces are continuously moving, slowly straining the faults in between. That strain is then eventually released in earthquakes. The innovative Southern California Integrated GPS Network (SCIGN) tracks the motions of these pieces over most of southern California with unprecedented precision. This new network greatly improves the ability to assess seismic hazards and quickly measure the larger displacements that occur during and immediatelyafter earthquakes.

Neural net target-tracking system using structured laser patterns

NASA Astrophysics Data System (ADS)

Cho, Jae-Wan; Lee, Yong-Bum; Lee, Nam-Ho; Park, Soon-Yong; Lee, Jongmin; Choi, Gapchu; Baek, Sunghyun; Park, Dong-Sun

1996-06-01

In this paper, we describe a robot endeffector tracking system using sensory information from recently-announced structured pattern laser diodes, which can generate images with several different types of structured pattern. The neural network approach is employed to recognize the robot endeffector covering the situation of three types of motion: translation, scaling and rotation. Features for the neural network to detect the position of the endeffector are extracted from the preprocessed images. Artificial neural networks are used to store models and to match with unknown input features recognizing the position of the robot endeffector. Since a minimal number of samples are used for different directions of the robot endeffector in the system, an artificial neural network with the generalization capability can be utilized for unknown input features. A feedforward neural network with the generalization capability can be utilized for unknown input features. A feedforward neural network trained with the back propagation learning is used to detect the position of the robot endeffector. Another feedforward neural network module is used to estimate the motion from a sequence of images and to control movements of the robot endeffector. COmbining the tow neural networks for recognizing the robot endeffector and estimating the motion with the preprocessing stage, the whole system keeps tracking of the robot endeffector effectively.

Handheld portable real-time tracking and communications device

DOEpatents

Wiseman, James M [Albuquerque, NM; Riblett, Jr., Loren E.; Green, Karl L [Albuquerque, NM; Hunter, John A [Albuquerque, NM; Cook, III, Robert N.; Stevens, James R [Arlington, VA

2012-05-22

Portable handheld real-time tracking and communications devices include; a controller module, communications module including global positioning and mesh network radio module, data transfer and storage module, and a user interface module enclosed in a water-resistant enclosure. Real-time tracking and communications devices can be used by protective force, security and first responder personnel to provide situational awareness allowing for enhance coordination and effectiveness in rapid response situations. Such devices communicate to other authorized devices via mobile ad-hoc wireless networks, and do not require fixed infrastructure for their operation.

Satellite-tracking and earth-dynamics research programs

NASA Technical Reports Server (NTRS)

1975-01-01

The activities and progress in the satellite tracking and earth dynamics research during the first half of calendar year 1975 are described. Satellite tracking network operations, satellite geodesy and geophysics programs, GEOS 3 project support, and atmospheric research are covered.

The deep space network

NASA Technical Reports Server (NTRS)

1977-01-01

A Deep Space Network progress report is presented dealing with in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations.

Dishing Up the Data: The Role of Australian Space Tracking and Radioastronomy Facilities in the Exploration of the Solar System

NASA Astrophysics Data System (ADS)

Dougherty, K.; Sarkissian, J.

2002-01-01

The recent Australian film, The Dish, highlighted the role played by the Parkes Radio Telescope in tracking and communicating with the Apollo 11 mission. However the events depicted in this film represent only a single snapshot of the role played by Australian radio astronomy and space tracking facilities in the exploration of the Solar System. In 1960, NASA established its first deep space tracking station outside the United States at Island Lagoon, near Woomera in South Australia. From 1961 until 1972, this station was an integral part of the Deep Space Network, responsible for tracking and communicating with NASA's interplanetary spacecraft. It was joined in 1965 by the Tidbinbilla tracking station, located near Canberra in eastern Australia, a major DSN facility that is still in operation today. Other NASA tracking facilities (for the STADAN and Manned Space Flight networks) were also established in Australia during the 1960s, making this country home to the largest number of NASA tracking facilities outside the United States. At the same time as the Island Lagoon station was being established in South Australia, one of the world's major radio telescope facilities was being established at Parkes, in western New South Wales. This 64-metre diameter dish, designed and operated by the Commonwealth Scientific and Industrial Research Organisation (CSIRO), was also well-suited for deep space tracking work: its design was, in fact, adapted by NASA for the 64-metre dishes of the Deep Space Network. From Mariner II in 1962 until today, the Parkes Radio Telescope has been contracted by NASA on many occasions to support interplanetary spacecraft, as well as the Apollo lunar missions. This paper will outline the role played by both the Parkes Radio Telescope and the NASA facilities based in Australia in the exploration of the Solar System between 1960 and 1976, when the Viking missions landed on Mars. It will outline the establishment and operation of the Deep Space Network in Australia and consider the joint US-Australian agreement under which it was managed. It will also discuss the relationship of the NASA stations to the Parkes Radio Telescope and the integration of Parkes into the NASA network to support specific space missions. The particular involvement of Australian facilities in significant space missions will be highlighted and assessed.

An improved multi-domain convolution tracking algorithm

NASA Astrophysics Data System (ADS)

Sun, Xin; Wang, Haiying; Zeng, Yingsen

2018-04-01

Along with the wide application of the Deep Learning in the field of Computer vision, Deep learning has become a mainstream direction in the field of object tracking. The tracking algorithm in this paper is based on the improved multidomain convolution neural network, and the VOT video set is pre-trained on the network by multi-domain training strategy. In the process of online tracking, the network evaluates candidate targets sampled from vicinity of the prediction target in the previous with Gaussian distribution, and the candidate target with the highest score is recognized as the prediction target of this frame. The Bounding Box Regression model is introduced to make the prediction target closer to the ground-truths target box of the test set. Grouping-update strategy is involved to extract and select useful update samples in each frame, which can effectively prevent over fitting. And adapt to changes in both target and environment. To improve the speed of the algorithm while maintaining the performance, the number of candidate target succeed in adjusting dynamically with the help of Self-adaption parameter Strategy. Finally, the algorithm is tested by OTB set, compared with other high-performance tracking algorithms, and the plot of success rate and the accuracy are drawn. which illustrates outstanding performance of the tracking algorithm in this paper.

On-track testing of a power harvesting device for railroad track health monitoring

NASA Astrophysics Data System (ADS)

Hansen, Sean E.; Pourghodrat, Abolfazl; Nelson, Carl A.; Fateh, Mahmood

2010-03-01

A considerable proportion of railroad infrastructure exists in regions which are comparatively remote. With regard to the cost of extending electrical infrastructure into these areas, road crossings in these areas do not have warning light systems or crossing gates and are commonly marked with reflective signage. For railroad track health monitoring purposes, distributed sensor networks can be applicable in remote areas, but the same limitation regarding electrical infrastructure is the hindrance. This motivated the development of an energy harvesting solution for remote railroad deployment. This paper describes on-track experimental testing of a mechanical device for harvesting mechanical power from passing railcar traffic, in view of supplying electrical power to warning light systems at crossings and to remote networks of sensors. The device is mounted to and spans two rail ties and transforms the vertical rail displacement into electrical energy through mechanical amplification and rectification into a PMDC generator. A prototype was tested under loaded and unloaded railcar traffic at low speeds. Stress analysis and speed scaling analysis are presented, results of the on-track tests are compared and contrasted to previous laboratory testing, discrepancies between the two are explained, and conclusions are drawn regarding suitability of the device for illuminating high-efficiency LED lights at railroad crossings and powering track-health sensor networks.

The administration of the NASA space tracking system and the NASA space tracking system in Australia

NASA Technical Reports Server (NTRS)

Hollander, N.

1973-01-01

The international activities of the NASA space program were studied with emphasis on the development and maintenance of tracking stations in Australia. The history and administration of the tracking organization and the manning policies for the stations are discussed, and factors affecting station operation are appraised. A field study of the Australian tracking network is included.

GOATS 2011 Adaptive and Collaborative Exploitation of 3-Dimensional Environmental Acoustics in Distributed Undersea Networks

DTIC Science & Technology

2013-09-30

Figure 13. The Unicorn AUV (yellow track) tracking a static temperature front between 18°C (blue- shaded region) and 19°C (green-shaded region...along the Mid-Atlantic Bight shelf break front in a modified MSEAS ocean model. Unicorn tracked the front southeast over 55 km (as the crow flies...robustness of the front tracking behavior. 15 Figure 14. The Unicorn AUV (yellow track) and Macrura AUV (magenta track) tracking a dynamic

Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks.

PubMed

Schmidt, Christoph; Piper, Diana; Pester, Britta; Mierau, Andreas; Witte, Herbert

2018-05-01

Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework's potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.

Optimal modified tracking performance for MIMO networked control systems with communication constraints.

PubMed

Wu, Jie; Zhou, Zhu-Jun; Zhan, Xi-Sheng; Yan, Huai-Cheng; Ge, Ming-Feng

2017-05-01

This paper investigates the optimal modified tracking performance of multi-input multi-output (MIMO) networked control systems (NCSs) with packet dropouts and bandwidth constraints. Some explicit expressions are obtained by using co-prime factorization and the spectral decomposition technique. The obtained results show that the optimal modified tracking performance is related to the intrinsic properties of a given plant such as non-minimum phase (NMP) zeros, unstable poles, and their directions. Furthermore, the modified factor, packet dropouts probability and bandwidth also impact the optimal modified tracking performance of the NCSs. The optimal modified tracking performance with channel input power constraint is obtained by searching through all stabilizing two-parameter compensator. Finally, some typical examples are given to illustrate the effectiveness of the theoretical results. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

The effect of tracking network configuration on Global Positioning System (GPS) baseline estimates for the CASA (Central and South America) Uno experiment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wolf, S.K.; Dixon, T.H.; Freymueller, J.T.

1990-04-01

Geodetic monitoring of subduction of the Nazca and Cocos plates is a goal of the CASA (Central and South America) Global Positioning System (GPS) experiments, and requires measurement of intersite distances (baselines) in excess of 500 km. The major error source in these measurements is the uncertainty in the position of the GPS satellites at the time of observation. A key aspect of the first CASA experiment, CASA Uno, was the initiation of a global network of tracking stations minimize these errors. The authors studied the effect of using various subsets of this global tracking network on long (>100 km)more » baseline estimates in the CASA region. Best results were obtained with a global tracking network consisting of three U.S. fiducial stations, two sites in the southwest pacific and two sites in Europe. Relative to smaller subsets, this global network improved baseline repeatability, resolution of carrier phase cycle ambiguities, and formal errors of the orbit estimates. Describing baseline repeatability for horizontal components as {sigma}=(a{sup 2} + b{sup 2}L{sup 2}){sup 1/2} where L is baseline length, the authors obtained a = 4 and 9 mm and b = 2.8{times}10{sup {minus}8} and 2.3{times}10{sup {minus}8} for north and east components, respectively, on CASA baselines up to 1,000 km in length with this global network.« less

Tracking and Treating Mobile Populations. The TB Net System. Migrant Clinicians Network Monograph Series. = El Sistema de Red para la TB.

ERIC Educational Resources Information Center

Migrant Clinicians Network, Inc., Austin, TX.

A comprehensive tracking and referral network that helps provide continuity of care for mobile populations with active tuberculosis (TB) or TB infection is considered essential for effective treatment of TB. However, the interstate referral system that exists between state health departments has been highly inefficient for serving migrant…

Neural network disturbance observer-based distributed finite-time formation tracking control for multiple unmanned helicopters.

PubMed

Wang, Dandan; Zong, Qun; Tian, Bailing; Shao, Shikai; Zhang, Xiuyun; Zhao, Xinyi

2018-02-01

The distributed finite-time formation tracking control problem for multiple unmanned helicopters is investigated in this paper. The control object is to maintain the positions of follower helicopters in formation with external interferences. The helicopter model is divided into a second order outer-loop subsystem and a second order inner-loop subsystem based on multiple-time scale features. Using radial basis function neural network (RBFNN) technique, we first propose a novel finite-time multivariable neural network disturbance observer (FMNNDO) to estimate the external disturbance and model uncertainty, where the neural network (NN) approximation errors can be dynamically compensated by adaptive law. Next, based on FMNNDO, a distributed finite-time formation tracking controller and a finite-time attitude tracking controller are designed using the nonsingular fast terminal sliding mode (NFTSM) method. In order to estimate the second derivative of the virtual desired attitude signal, a novel finite-time sliding mode integral filter is designed. Finally, Lyapunov analysis and multiple-time scale principle ensure the realization of control goal in finite-time. The effectiveness of the proposed FMNNDO and controllers are then verified by numerical simulations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive neural network motion control of manipulators with experimental evaluations.

PubMed

Puga-Guzmán, S; Moreno-Valenzuela, J; Santibáñez, V

2014-01-01

A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller.

Adaptive Neural Network Motion Control of Manipulators with Experimental Evaluations

PubMed Central

Puga-Guzmán, S.; Moreno-Valenzuela, J.; Santibáñez, V.

2014-01-01

A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller. PMID:24574910

Action-Driven Visual Object Tracking With Deep Reinforcement Learning.

PubMed

Yun, Sangdoo; Choi, Jongwon; Yoo, Youngjoon; Yun, Kimin; Choi, Jin Young

2018-06-01

In this paper, we propose an efficient visual tracker, which directly captures a bounding box containing the target object in a video by means of sequential actions learned using deep neural networks. The proposed deep neural network to control tracking actions is pretrained using various training video sequences and fine-tuned during actual tracking for online adaptation to a change of target and background. The pretraining is done by utilizing deep reinforcement learning (RL) as well as supervised learning. The use of RL enables even partially labeled data to be successfully utilized for semisupervised learning. Through the evaluation of the object tracking benchmark data set, the proposed tracker is validated to achieve a competitive performance at three times the speed of existing deep network-based trackers. The fast version of the proposed method, which operates in real time on graphics processing unit, outperforms the state-of-the-art real-time trackers with an accuracy improvement of more than 8%.

Wireless sensor networks for heritage object deformation detection and tracking algorithm.

PubMed

Xie, Zhijun; Huang, Guangyan; Zarei, Roozbeh; He, Jing; Zhang, Yanchun; Ye, Hongwu

2014-10-31

Deformation is the direct cause of heritage object collapse. It is significant to monitor and signal the early warnings of the deformation of heritage objects. However, traditional heritage object monitoring methods only roughly monitor a simple-shaped heritage object as a whole, but cannot monitor complicated heritage objects, which may have a large number of surfaces inside and outside. Wireless sensor networks, comprising many small-sized, low-cost, low-power intelligent sensor nodes, are more useful to detect the deformation of every small part of the heritage objects. Wireless sensor networks need an effective mechanism to reduce both the communication costs and energy consumption in order to monitor the heritage objects in real time. In this paper, we provide an effective heritage object deformation detection and tracking method using wireless sensor networks (EffeHDDT). In EffeHDDT, we discover a connected core set of sensor nodes to reduce the communication cost for transmitting and collecting the data of the sensor networks. Particularly, we propose a heritage object boundary detecting and tracking mechanism. Both theoretical analysis and experimental results demonstrate that our EffeHDDT method outperforms the existing methods in terms of network traffic and the precision of the deformation detection.

Wireless Sensor Networks for Heritage Object Deformation Detection and Tracking Algorithm

PubMed Central

Xie, Zhijun; Huang, Guangyan; Zarei, Roozbeh; He, Jing; Zhang, Yanchun; Ye, Hongwu

2014-01-01

Deformation is the direct cause of heritage object collapse. It is significant to monitor and signal the early warnings of the deformation of heritage objects. However, traditional heritage object monitoring methods only roughly monitor a simple-shaped heritage object as a whole, but cannot monitor complicated heritage objects, which may have a large number of surfaces inside and outside. Wireless sensor networks, comprising many small-sized, low-cost, low-power intelligent sensor nodes, are more useful to detect the deformation of every small part of the heritage objects. Wireless sensor networks need an effective mechanism to reduce both the communication costs and energy consumption in order to monitor the heritage objects in real time. In this paper, we provide an effective heritage object deformation detection and tracking method using wireless sensor networks (EffeHDDT). In EffeHDDT, we discover a connected core set of sensor nodes to reduce the communication cost for transmitting and collecting the data of the sensor networks. Particularly, we propose a heritage object boundary detecting and tracking mechanism. Both theoretical analysis and experimental results demonstrate that our EffeHDDT method outperforms the existing methods in terms of network traffic and the precision of the deformation detection. PMID:25365458

An automated method for the evaluation of the pointing accuracy of Sun-tracking devices

NASA Astrophysics Data System (ADS)

Baumgartner, Dietmar J.; Pötzi, Werner; Freislich, Heinrich; Strutzmann, Heinz; Veronig, Astrid M.; Rieder, Harald E.

2017-03-01

The accuracy of solar radiation measurements, for direct (DIR) and diffuse (DIF) radiation, depends significantly on the precision of the operational Sun-tracking device. Thus, rigid targets for instrument performance and operation have been specified for international monitoring networks, e.g., the Baseline Surface Radiation Network (BSRN) operating under the auspices of the World Climate Research Program (WCRP). Sun-tracking devices that fulfill these accuracy requirements are available from various instrument manufacturers; however, none of the commercially available systems comprise an automatic accuracy control system allowing platform operators to independently validate the pointing accuracy of Sun-tracking sensors during operation. Here we present KSO-STREAMS (KSO-SunTRackEr Accuracy Monitoring System), a fully automated, system-independent, and cost-effective system for evaluating the pointing accuracy of Sun-tracking devices. We detail the monitoring system setup, its design and specifications, and the results from its application to the Sun-tracking system operated at the Kanzelhöhe Observatory (KSO) Austrian radiation monitoring network (ARAD) site. The results from an evaluation campaign from March to June 2015 show that the tracking accuracy of the device operated at KSO lies within BSRN specifications (i.e., 0.1° tracking accuracy) for the vast majority of observations (99.8 %). The evaluation of manufacturer-specified active-tracking accuracies (0.02°), during periods with direct solar radiation exceeding 300 W m-2, shows that these are satisfied in 72.9 % of observations. Tracking accuracies are highest during clear-sky conditions and on days where prevailing clear-sky conditions are interrupted by frontal movement; in these cases, we obtain the complete fulfillment of BSRN requirements and 76.4 % of observations within manufacturer-specified active-tracking accuracies. Limitations to tracking surveillance arise during overcast conditions and periods of partial solar-limb coverage by clouds. On days with variable cloud cover, 78.1 % (99.9 %) of observations meet active-tracking (BSRN) accuracy requirements while for days with prevailing overcast conditions these numbers reduce to 64.3 % (99.5 %).

A history of the deep space network

NASA Technical Reports Server (NTRS)

Corliss, W. R.

1976-01-01

The Deep Space Network (DSN) has been managed and operated by the Jet Propulsion Laboratory (JPL) under NASA contract ever since NASA was formed in late 1958. The Tracking and data acquisition tasks of the DSN are markedly different from those of the other NASA network, STDN. STDN, which is an amalgamation of the satellite tracking network (STADAN) and the Manned Space Flight Network (MSFN), is primarily concerned with supporting manned and unmanned earth satellites. In contrast, the DSN deals with spacecraft that are thousands to hundreds of millions of miles away. The radio signals from these distant craft are many orders of magnitude weaker than those from nearby satellites. Distance also makes precise radio location more difficult; and accurate trajectory data are vital to deep space navigation in the vicinities of the other planets of the solar system. In addition to tracking spacecraft and acquiring data from them, the DSN is required to transmit many thousands of commands to control the sophisticated planetary probes and interplanetary monitoring stations. To meet these demanding requirements, the DSN has been compelled to be in the forefront of technology.

Real-time camera-based face detection using a modified LAMSTAR neural network system

NASA Astrophysics Data System (ADS)

Girado, Javier I.; Sandin, Daniel J.; DeFanti, Thomas A.; Wolf, Laura K.

2003-03-01

This paper describes a cost-effective, real-time (640x480 at 30Hz) upright frontal face detector as part of an ongoing project to develop a video-based, tetherless 3D head position and orientation tracking system. The work is specifically targeted for auto-stereoscopic displays and projection-based virtual reality systems. The proposed face detector is based on a modified LAMSTAR neural network system. At the input stage, after achieving image normalization and equalization, a sub-window analyzes facial features using a neural network. The sub-window is segmented, and each part is fed to a neural network layer consisting of a Kohonen Self-Organizing Map (SOM). The output of the SOM neural networks are interconnected and related by correlation-links, and can hence determine the presence of a face with enough redundancy to provide a high detection rate. To avoid tracking multiple faces simultaneously, the system is initially trained to track only the face centered in a box superimposed on the display. The system is also rotationally and size invariant to a certain degree.

The deep space network

NASA Technical Reports Server (NTRS)

1980-01-01

The functions and facilities of the Deep Space Network are considered. Progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is reported.

The deep space network

NASA Technical Reports Server (NTRS)

1979-01-01

Progress is reported in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations. The functions and facilities of the Deep Space Network are emphasized.

Deployment Design of Wireless Sensor Network for Simple Multi-Point Surveillance of a Moving Target

PubMed Central

Tsukamoto, Kazuya; Ueda, Hirofumi; Tamura, Hitomi; Kawahara, Kenji; Oie, Yuji

2009-01-01

In this paper, we focus on the problem of tracking a moving target in a wireless sensor network (WSN), in which the capability of each sensor is relatively limited, to construct large-scale WSNs at a reasonable cost. We first propose two simple multi-point surveillance schemes for a moving target in a WSN and demonstrate that one of the schemes can achieve high tracking probability with low power consumption. In addition, we examine the relationship between tracking probability and sensor density through simulations, and then derive an approximate expression representing the relationship. As the results, we present guidelines for sensor density, tracking probability, and the number of monitoring sensors that satisfy a variety of application demands. PMID:22412326

Self-organizing radial basis function networks for adaptive flight control and aircraft engine state estimation

NASA Astrophysics Data System (ADS)

Shankar, Praveen

The performance of nonlinear control algorithms such as feedback linearization and dynamic inversion is heavily dependent on the fidelity of the dynamic model being inverted. Incomplete or incorrect knowledge of the dynamics results in reduced performance and may lead to instability. Augmenting the baseline controller with approximators which utilize a parametrization structure that is adapted online reduces the effect of this error between the design model and actual dynamics. However, currently existing parameterizations employ a fixed set of basis functions that do not guarantee arbitrary tracking error performance. To address this problem, we develop a self-organizing parametrization structure that is proven to be stable and can guarantee arbitrary tracking error performance. The training algorithm to grow the network and adapt the parameters is derived from Lyapunov theory. In addition to growing the network of basis functions, a pruning strategy is incorporated to keep the size of the network as small as possible. This algorithm is implemented on a high performance flight vehicle such as F-15 military aircraft. The baseline dynamic inversion controller is augmented with a Self-Organizing Radial Basis Function Network (SORBFN) to minimize the effect of the inversion error which may occur due to imperfect modeling, approximate inversion or sudden changes in aircraft dynamics. The dynamic inversion controller is simulated for different situations including control surface failures, modeling errors and external disturbances with and without the adaptive network. A performance measure of maximum tracking error is specified for both the controllers a priori. Excellent tracking error minimization to a pre-specified level using the adaptive approximation based controller was achieved while the baseline dynamic inversion controller failed to meet this performance specification. The performance of the SORBFN based controller is also compared to a fixed RBF network based adaptive controller. While the fixed RBF network based controller which is tuned to compensate for control surface failures fails to achieve the same performance under modeling uncertainty and disturbances, the SORBFN is able to achieve good tracking convergence under all error conditions.

Analysis on Tracking Schedule and Measurements Characteristics for the Spacecraft on the Phase of Lunar Transfer and Capture

NASA Astrophysics Data System (ADS)

Song, Young-Joo; Choi, Su-Jin; Ahn, Sang-il; Sim, Eun-Sup

2014-03-01

In this work, the preliminary analysis on both the tracking schedule and measurements characteristics for the spacecraft on the phase of lunar transfer and capture is performed. To analyze both the tracking schedule and measurements characteristics, lunar transfer and capture phases¡¯ optimized trajectories are directly adapted from former research, and eleven ground tracking facilities (three Deep Space Network sties, seven Near Earth Network sites, one Daejeon site) are assumed to support the mission. Under these conceptual mission scenarios, detailed tracking schedules and expected measurement characteristics during critical maneuvers (Trans Lunar Injection, Lunar Orbit Insertion and Apoapsis Adjustment Maneuver), especially for the Deajeon station, are successfully analyzed. The orders of predicted measurements' variances during lunar capture phase according to critical maneuvers are found to be within the order of mm/s for the range and micro-deg/s for the angular measurements rates which are in good agreement with the recommended values of typical measurement modeling accuracies for Deep Space Networks. Although preliminary navigation accuracy guidelines are provided through this work, it is expected to give more practical insights into preparing the Korea's future lunar mission, especially for developing flight dynamics subsystem.

Madrid space station

NASA Technical Reports Server (NTRS)

Fahnestock, R. J.; Renzetti, N. A.

1975-01-01

The Madrid space station, operated under bilateral agreements between the governments of the United States and Spain, is described in both Spanish and English. The space station utilizes two tracking and data acquisition networks: the Deep Space Network (DSN) of the National Aeronautics and Space Administration and the Spaceflight Tracking and Data Network (STDN) operated under the direction of the Goddard Space Flight Center. The station, which is staffed by Spanish employees, comprises four facilities: Robledo 1, Cebreros, and Fresnedillas-Navalagamella, all with 26-meter-diameter antennas, and Robledo 2, with a 64-meter antenna.

The Deep Space Network. [tracking and communication functions and facilities

NASA Technical Reports Server (NTRS)

1974-01-01

The objectives, functions, and organization of the Deep Space Network are summarized. The Deep Space Instrumentation Facility, the Ground Communications Facility, and the Network Control System are described.

The deep space network

NASA Technical Reports Server (NTRS)

1979-01-01

A report is given of the Deep Space Networks progress in (1) flight project support, (2) tracking and data acquisition research and technology, (3) network engineering, (4) hardware and software implementation, and (5) operations.

The use of cell phone network data in traffic data collection and long-haul truckshed (geographic extent) tracking.

DOT National Transportation Integrated Search

2012-12-01

This study analyzed the potential of cell phone positioning techniques in freight truck data collection and long-haul : truckshed (geographic extent) tracking. Freight truck identification and tracking algorithms were developed by means of : cell pho...

Ngi and Internet2: accelerating the creation of tomorrow's internet.

PubMed

Kratz, M; Ackerman, M; Hanss, T; Corbato, S

2001-01-01

Internet2 is a consortium of leading U.S. universities working in partnership with industry and the U.S. government's Next Generation Internet (NGI) initiative to develop a faster, more reliable Internet for research and education including enhanced, high-performance networking services and the advanced applications that are enabled by those services [1]. By facilitating and coordinating the development, deployment, operation, and technology transfer of advanced, network-based applications and network services, Internet2 and NGI are working together to fundamentally change the way scientists, engineers, clinicians, and others work together. [http://www.internet2.edu] The NGI Program has three tracks: research, network testbeds, and applications. The aim of the research track is to promote experimentation with the next generation of network technologies. The network testbed track aims to develop next generation network testbeds to connect universities and federal research institutions at speeds that are sufficient to demonstrate new technologies and support future research. The aim of the applications track is to demonstrate new applications, enabled by the NGI networks, to meet important national goals and missions [2]. [http://www.ngi.gov/] The Internet2/NGI backbone networks, Abilene and vBNS (very high performance Backbone Network Service), provide the basis of collaboration and development for a new breed of advanced medical applications. Academic medical centers leverage the resources available throughout the Internet2 high-performance networking community for high-capacity broadband and selectable quality of service to make effective use of national repositories. The Internet2 Health Sciences Initiative enables a new generation of emerging medical applications whose architecture and development have been restricted by or are beyond the constraints of traditional Internet environments. These initiatives facilitate a variety of activities to foster the development and deployment of emerging applications that meet the requirements of clinical practice, medical and related biological research, education, and medical awareness throughout the public sector. Medical applications that work with high performance networks and supercomputing capabilities offer exciting new solutions for the medical industry. Internet2 and NGI,strive to combine the expertise of their constituents to establish a distributed knowledge system for achieving innovation in research, teaching, learning, and clinical care.

Age-related differences in brain network activation and co-activation during multiple object tracking.

PubMed

Dørum, Erlend S; Alnæs, Dag; Kaufmann, Tobias; Richard, Geneviève; Lund, Martina J; Tønnesen, Siren; Sneve, Markus H; Mathiesen, Nina C; Rustan, Øyvind G; Gjertsen, Øivind; Vatn, Sigurd; Fure, Brynjar; Andreassen, Ole A; Nordvik, Jan Egil; Westlye, Lars T

2016-11-01

Multiple object tracking (MOT) is a powerful paradigm for measuring sustained attention. Although previous fMRI studies have delineated the brain activation patterns associated with tracking and documented reduced tracking performance in aging, age-related effects on brain activation during MOT have not been characterized. In particular, it is unclear if the task-related activation of different brain networks is correlated, and also if this coordination between activations within brain networks shows differential effects of age. We obtained fMRI data during MOT at two load conditions from a group of younger ( n  = 25, mean age = 24.4 ± 5.1 years) and older ( n  = 21, mean age = 64.7 ± 7.4 years) healthy adults. Using a combination of voxel-wise and independent component analysis, we investigated age-related differences in the brain network activation. In order to explore to which degree activation of the various brain networks reflect unique and common mechanisms, we assessed the correlations between the brain networks' activations. Behavioral performance revealed an age-related reduction in MOT accuracy. Voxel and brain network level analyses converged on decreased load-dependent activations of the dorsal attention network (DAN) and decreased load-dependent deactivations of the default mode networks (DMN) in the old group. Lastly, we found stronger correlations in the task-related activations within DAN and within DMN components for younger adults, and stronger correlations between DAN and DMN components for older adults. Using MOT as means for measuring attentional performance, we have demonstrated an age-related attentional decline. Network-level analysis revealed age-related alterations in network recruitment consisting of diminished activations of DAN and diminished deactivations of DMN in older relative to younger adults. We found stronger correlations within DMN and within DAN components for younger adults and stronger correlations between DAN and DMN components for older adults, indicating age-related alterations in the coordinated network-level activation during attentional processing.

Tracking strategies for laser ranging to multiple satellite targets

NASA Technical Reports Server (NTRS)

Robbins, J. W.; Smith, D. E.; Kolenkiewicz, R.

1994-01-01

By the middle of the decade, several new Laser Geodynamic Satellites will be launched to join the current constellation comprised of the laser geodynamic satellite (LAGEOS) (US), Starlette (France), Ajisai (Japan), and Etalon I and II (USSR). The satellites to be launched, LAGEOS II and III (US & Italy), and Stella (France), will be injected into orbits that differ from the existing constellation so that geodetic and gravimetric quantities are sampled to enhance their resolution and accuracy. An examination of various possible tracking strategies adopted by the network of laser tracking stations has revealed that the recovery of precise geodetic parameters can be obtained over shorter intervals than is currently obtainable with the present constellation of satellites. This is particularly important in the planning of mobile laser tracking operations, given a network of permanently operating tracking sites. Through simulations, it is shown that laser tracking of certain satellite passes, pre-selected to provide optimal sky-coverage, provides the means to acquire a sufficient amount of data to allow the recovery of 1 cm station positions.

Low Complexity Track Initialization and Fusion for Multi-Modal Sensor Networks

DTIC Science & Technology

2012-11-08

feature was demonstrated via the simulations. Aerospace 2011work further documents our investigation of multiple target tracking filters in...bounds that determine how well a sensor network can resolve and localize multiple targets as a function of the operating parameters such as sensor...probability density (PHD) filter for binary measurements using proximity sensors. 15. SUBJECT TERMS proximity sensors, PHD filter, multiple

Health and Environment Linked for Information Exchange in Atlanta (HELIX-Atlanta): A Pilot Tracking System

NASA Technical Reports Server (NTRS)

Rickman, Doug; Shire, J.; Qualters, J.; Mitchell, K.; Pollard, S.; Rao, R.; Kajumba, N.; Quattrochi, D.; Estes, M., Jr.; Meyer, P.;

Functional Network Disruption in the Degenerative Dementias

PubMed Central

Pievani, Michela; de Haan, Willem; Wu, Tao; Seeley, William W; Frisoni, Giovanni B

2011-01-01

Despite considerable advances toward understanding the molecular pathophysiology of the neurodegenerative dementias, the mechanisms linking molecular changes to neuropathology and the latter to clinical symptoms remain largely obscure. Connectivity is a distinctive feature of the brain and the integrity of functional network dynamics is critical for normal functioning. A better understanding of network disruption in the neurodegenerative dementias may help bridge the gap between molecular changes, pathology and symptoms. Recent findings on functional network disruption as assessed with “resting-state” or intrinsic connectivity fMRI and EEG/MEG have shown distinct patterns of network disruption across the major neurodegenerative diseases. These network abnormalities are relatively specific to the clinical syndromes, and in Alzheimer's disease and frontotemporal dementia network disruption tracks the pattern of pathological changes. These findings may have a practical impact on diagnostic accuracy, allowing earlier detection of neurodegenerative diseases even at the pre-symptomatic stage, and tracking of disease progression. PMID:21778116

VTAC: virtual terrain assisted impact assessment for cyber attacks

NASA Astrophysics Data System (ADS)

Argauer, Brian J.; Yang, Shanchieh J.

2008-03-01

Overwhelming intrusion alerts have made timely response to network security breaches a difficult task. Correlating alerts to produce a higher level view of intrusion state of a network, thus, becomes an essential element in network defense. This work proposes to analyze correlated or grouped alerts and determine their 'impact' to services and users of the network. A network is modeled as 'virtual terrain' where cyber attacks maneuver. Overlaying correlated attack tracks on virtual terrain exhibits the vulnerabilities exploited by each track and the relationships between them and different network entities. The proposed impact assessment algorithm utilizes the graph-based virtual terrain model and combines assessments of damages caused by the attacks. The combined impact scores allow to identify severely damaged network services and affected users. Several scenarios are examined to demonstrate the uses of the proposed Virtual Terrain Assisted Impact Assessment for Cyber Attacks (VTAC).

Unsupervised learning in persistent sensing for target recognition by wireless ad hoc networks of ground-based sensors

NASA Astrophysics Data System (ADS)

Hortos, William S.

2008-04-01

In previous work by the author, effective persistent and pervasive sensing for recognition and tracking of battlefield targets were seen to be achieved, using intelligent algorithms implemented by distributed mobile agents over a composite system of unmanned aerial vehicles (UAVs) for persistence and a wireless network of unattended ground sensors for pervasive coverage of the mission environment. While simulated performance results for the supervised algorithms of the composite system are shown to provide satisfactory target recognition over relatively brief periods of system operation, this performance can degrade by as much as 50% as target dynamics in the environment evolve beyond the period of system operation in which the training data are representative. To overcome this limitation, this paper applies the distributed approach using mobile agents to the network of ground-based wireless sensors alone, without the UAV subsystem, to provide persistent as well as pervasive sensing for target recognition and tracking. The supervised algorithms used in the earlier work are supplanted by unsupervised routines, including competitive-learning neural networks (CLNNs) and new versions of support vector machines (SVMs) for characterization of an unknown target environment. To capture the same physical phenomena from battlefield targets as the composite system, the suite of ground-based sensors can be expanded to include imaging and video capabilities. The spatial density of deployed sensor nodes is increased to allow more precise ground-based location and tracking of detected targets by active nodes. The "swarm" mobile agents enabling WSN intelligence are organized in a three processing stages: detection, recognition and sustained tracking of ground targets. Features formed from the compressed sensor data are down-selected according to an information-theoretic algorithm that reduces redundancy within the feature set, reducing the dimension of samples used in the target recognition and tracking routines. Target tracking is based on simplified versions of Kalman filtration. Accuracy of recognition and tracking of implemented versions of the proposed suite of unsupervised algorithms is somewhat degraded from the ideal. Target recognition and tracking by supervised routines and by unsupervised SVM and CLNN routines in the ground-based WSN is evaluated in simulations using published system values and sensor data from vehicular targets in ground-surveillance scenarios. Results are compared with previously published performance for the system of the ground-based sensor network (GSN) and UAV swarm.

An Effective and Robust Decentralized Target Tracking Scheme in Wireless Camera Sensor Networks.

PubMed

Fu, Pengcheng; Cheng, Yongbo; Tang, Hongying; Li, Baoqing; Pei, Jun; Yuan, Xiaobing

2017-03-20

In this paper, we propose an effective and robust decentralized tracking scheme based on the square root cubature information filter (SRCIF) to balance the energy consumption and tracking accuracy in wireless camera sensor networks (WCNs). More specifically, regarding the characteristics and constraints of camera nodes in WCNs, some special mechanisms are put forward and integrated in this tracking scheme. First, a decentralized tracking approach is adopted so that the tracking can be implemented energy-efficiently and steadily. Subsequently, task cluster nodes are dynamically selected by adopting a greedy on-line decision approach based on the defined contribution decision (CD) considering the limited energy of camera nodes. Additionally, we design an efficient cluster head (CH) selection mechanism that casts such selection problem as an optimization problem based on the remaining energy and distance-to-target. Finally, we also perform analysis on the target detection probability when selecting the task cluster nodes and their CH, owing to the directional sensing and observation limitations in field of view (FOV) of camera nodes in WCNs. From simulation results, the proposed tracking scheme shows an obvious improvement in balancing the energy consumption and tracking accuracy over the existing methods.

An Effective and Robust Decentralized Target Tracking Scheme in Wireless Camera Sensor Networks

PubMed Central

Fu, Pengcheng; Cheng, Yongbo; Tang, Hongying; Li, Baoqing; Pei, Jun; Yuan, Xiaobing

2017-01-01

In this paper, we propose an effective and robust decentralized tracking scheme based on the square root cubature information filter (SRCIF) to balance the energy consumption and tracking accuracy in wireless camera sensor networks (WCNs). More specifically, regarding the characteristics and constraints of camera nodes in WCNs, some special mechanisms are put forward and integrated in this tracking scheme. First, a decentralized tracking approach is adopted so that the tracking can be implemented energy-efficiently and steadily. Subsequently, task cluster nodes are dynamically selected by adopting a greedy on-line decision approach based on the defined contribution decision (CD) considering the limited energy of camera nodes. Additionally, we design an efficient cluster head (CH) selection mechanism that casts such selection problem as an optimization problem based on the remaining energy and distance-to-target. Finally, we also perform analysis on the target detection probability when selecting the task cluster nodes and their CH, owing to the directional sensing and observation limitations in field of view (FOV) of camera nodes in WCNs. From simulation results, the proposed tracking scheme shows an obvious improvement in balancing the energy consumption and tracking accuracy over the existing methods. PMID:28335537

The deep space network

NASA Technical Reports Server (NTRS)

1977-01-01

The facilities, programming system, and monitor and control system for the deep space network are described. Ongoing planetary and interplanetary flight projects are reviewed, along with tracking and ground-based navigation, communications, and network and facility engineering.

Distributed multimodal data fusion for large scale wireless sensor networks

NASA Astrophysics Data System (ADS)

Ertin, Emre

2006-05-01

Sensor network technology has enabled new surveillance systems where sensor nodes equipped with processing and communication capabilities can collaboratively detect, classify and track targets of interest over a large surveillance area. In this paper we study distributed fusion of multimodal sensor data for extracting target information from a large scale sensor network. Optimal tracking, classification, and reporting of threat events require joint consideration of multiple sensor modalities. Multiple sensor modalities improve tracking by reducing the uncertainty in the track estimates as well as resolving track-sensor data association problems. Our approach to solving the fusion problem with large number of multimodal sensors is construction of likelihood maps. The likelihood maps provide a summary data for the solution of the detection, tracking and classification problem. The likelihood map presents the sensory information in an easy format for the decision makers to interpret and is suitable with fusion of spatial prior information such as maps, imaging data from stand-off imaging sensors. We follow a statistical approach to combine sensor data at different levels of uncertainty and resolution. The likelihood map transforms each sensor data stream to a spatio-temporal likelihood map ideally suitable for fusion with imaging sensor outputs and prior geographic information about the scene. We also discuss distributed computation of the likelihood map using a gossip based algorithm and present simulation results.

Automated target recognition and tracking using an optical pattern recognition neural network

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin

1991-01-01

The on-going development of an automatic target recognition and tracking system at the Jet Propulsion Laboratory is presented. This system is an optical pattern recognition neural network (OPRNN) that is an integration of an innovative optical parallel processor and a feature extraction based neural net training algorithm. The parallel optical processor provides high speed and vast parallelism as well as full shift invariance. The neural network algorithm enables simultaneous discrimination of multiple noisy targets in spite of their scales, rotations, perspectives, and various deformations. This fully developed OPRNN system can be effectively utilized for the automated spacecraft recognition and tracking that will lead to success in the Automated Rendezvous and Capture (AR&C) of the unmanned Cargo Transfer Vehicle (CTV). One of the most powerful optical parallel processors for automatic target recognition is the multichannel correlator. With the inherent advantages of parallel processing capability and shift invariance, multiple objects can be simultaneously recognized and tracked using this multichannel correlator. This target tracking capability can be greatly enhanced by utilizing a powerful feature extraction based neural network training algorithm such as the neocognitron. The OPRNN, currently under investigation at JPL, is constructed with an optical multichannel correlator where holographic filters have been prepared using the neocognitron training algorithm. The computation speed of the neocognitron-type OPRNN is up to 10(exp 14) analog connections/sec that enabling the OPRNN to outperform its state-of-the-art electronics counterpart by at least two orders of magnitude.

Neural network fusion capabilities for efficient implementation of tracking algorithms

NASA Astrophysics Data System (ADS)

Sundareshan, Malur K.; Amoozegar, Farid

1997-03-01

The ability to efficiently fuse information of different forms to facilitate intelligent decision making is one of the major capabilities of trained multilayer neural networks that is now being recognized. While development of innovative adaptive control algorithms for nonlinear dynamical plants that attempt to exploit these capabilities seems to be more popular, a corresponding development of nonlinear estimation algorithms using these approaches, particularly for application in target surveillance and guidance operations, has not received similar attention. We describe the capabilities and functionality of neural network algorithms for data fusion and implementation of tracking filters. To discuss details and to serve as a vehicle for quantitative performance evaluations, the illustrative case of estimating the position and velocity of surveillance targets is considered. Efficient target- tracking algorithms that can utilize data from a host of sensing modalities and are capable of reliably tracking even uncooperative targets executing fast and complex maneuvers are of interest in a number of applications. The primary motivation for employing neural networks in these applications comes from the efficiency with which more features extracted from different sensor measurements can be utilized as inputs for estimating target maneuvers. A system architecture that efficiently integrates the fusion capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described. The innovation lies in the way the fusion of multisensor data is accomplished to facilitate improved estimation without increasing the computational complexity of the dynamical state estimator itself.

The National Aeronautics and Space Administration (NASA) Tracking and Data Relay Satellite System (TDRSS) program Economic and programmatic, considerations

NASA Technical Reports Server (NTRS)

Aller, R. O.

1985-01-01

The Tracking and Data Relay Satellite System (TDRSS) represents the principal element of a new space-based tracking and communication network which will support NASA spaceflight missions in low earth orbit. In its complete configuration, the TDRSS network will include a space segment consisting of three highly specialized communication satellites in geosynchronous orbit, a ground segment consisting of an earth terminal, and associated data handling and control facilities. The TDRSS network has the objective to provide communication and data relay services between the earth-orbiting spacecraft and their ground-based mission control and data handling centers. The first TDRSS spacecraft has been now in service for two years. The present paper is concerned with the TDRSS experience from the perspective of the various programmatic and economic considerations which relate to the program.

N-CET: Network-Centric Exploitation and Tracking

DTIC Science & Technology

2009-10-01

DATES COVERED (From - To) October 2008 – August 2009 4 . TITLE AND SUBTITLE N-CET: NETWORK – CENTRIC EXPLOITATION AND TRACKING 5a. CONTRACT NUMBER...At the core of N-CET are information management services that decouple data producers and consumers , allowing reconfiguration to suit mission needs...Shown around the head-node are different pieces of hardware including the Sony PlayStation R©3 (PS3) nodes used for computationally demanding tasks

A radar-enabled collaborative sensor network integrating COTS technology for surveillance and tracking.

PubMed

Kozma, Robert; Wang, Lan; Iftekharuddin, Khan; McCracken, Ernest; Khan, Muhammad; Islam, Khandakar; Bhurtel, Sushil R; Demirer, R Murat

2012-01-01

The feasibility of using Commercial Off-The-Shelf (COTS) sensor nodes is studied in a distributed network, aiming at dynamic surveillance and tracking of ground targets. Data acquisition by low-cost (<$50 US) miniature low-power radar through a wireless mote is described. We demonstrate the detection, ranging and velocity estimation, classification and tracking capabilities of the mini-radar, and compare results to simulations and manual measurements. Furthermore, we supplement the radar output with other sensor modalities, such as acoustic and vibration sensors. This method provides innovative solutions for detecting, identifying, and tracking vehicles and dismounts over a wide area in noisy conditions. This study presents a step towards distributed intelligent decision support and demonstrates effectiveness of small cheap sensors, which can complement advanced technologies in certain real-life scenarios.

77 FR 25781 - Environmental Impact Statement; Washington, DC

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-01

... do not meet the needs of modern freight rail infrastructure due to its single track arrangement and the inability to accommodate double-stack intermodal container freight trains. The single-track... network that encompasses about 21,000 route miles of track in 23 states, the District of Columbia and the...

Noise reduction in urban LRT networks by combining track based solutions.

PubMed

Vogiatzis, Konstantinos; Vanhonacker, Patrick

2016-10-15

The overall objective of the Quiet-Track project is to provide step-changing track based noise mitigation and maintenance schemes for railway rolling noise in LRT (Light Rail Transit) networks. WP 4 in particular focuses on the combination of existing track based solutions to yield a global performance of at least 6dB(A). The validation was carried out using a track section in the network of Athens Metro Line 1 with an existing outside concrete slab track (RHEDA track) where high airborne rolling noise was observed. The procedure for the selection of mitigation measures is based on numerical simulations, combining WRNOISE and IMMI software tools for noise prediction with experimental determination of the required track and vehicle parameters (e.g., rail and wheel roughness). The availability of a detailed rolling noise calculation procedure allows for detailed designing of measures and of ranking individual measures. It achieves this by including the modelling of the wheel/rail source intensity and of the noise propagation with the ability to evaluate the effect of modifications at source level (e.g., grinding, rail dampers, wheel dampers, change in resiliency of wheels and/or rail fixation) and of modifications in the propagation path (absorption at the track base, noise barriers, screening). A relevant combination of existing solutions was selected in the function of the simulation results. Three distinct existing solutions were designed in detail aiming at a high rolling noise attenuation and not affecting the normal operation of the metro system: Action 1: implementation of sound absorbing precast elements (panel type) on the track bed, Action 2: implementation of an absorbing noise barrier with a height of 1.10-1.20m above rail level, and Action 3: installation of rail dampers. The selected solutions were implemented on site and the global performance was measured step by step for comparison with simulations. Copyright © 2015 Elsevier B.V. All rights reserved.

Network control processor for a TDMA system

NASA Astrophysics Data System (ADS)

Suryadevara, Omkarmurthy; Debettencourt, Thomas J.; Shulman, R. B.

Two unique aspects of designing a network control processor (NCP) to monitor and control a demand-assigned, time-division multiple-access (TDMA) network are described. The first involves the implementation of redundancy by synchronizing the databases of two geographically remote NCPs. The two sets of databases are kept in synchronization by collecting data on both systems, transferring databases, sending incremental updates, and the parallel updating of databases. A periodic audit compares the checksums of the databases to ensure synchronization. The second aspect involves the use of a tracking algorithm to dynamically reallocate TDMA frame space. This algorithm detects and tracks current and long-term load changes in the network. When some portions of the network are overloaded while others have excess capacity, the algorithm automatically calculates and implements a new burst time plan.

H∞ output tracking control of uncertain and disturbed nonlinear systems based on neural network model

NASA Astrophysics Data System (ADS)

Li, Chengcheng; Li, Yuefeng; Wang, Guanglin

2017-07-01

The work presented in this paper seeks to address the tracking problem for uncertain continuous nonlinear systems with external disturbances. The objective is to obtain a model that uses a reference-based output feedback tracking control law. The control scheme is based on neural networks and a linear difference inclusion (LDI) model, and a PDC structure and H∞ performance criterion are used to attenuate external disturbances. The stability of the whole closed-loop model is investigated using the well-known quadratic Lyapunov function. The key principles of the proposed approach are as follows: neural networks are first used to approximate nonlinearities, to enable a nonlinear system to then be represented as a linearised LDI model. An LMI (linear matrix inequality) formula is obtained for uncertain and disturbed linear systems. This formula enables a solution to be obtained through an interior point optimisation method for some nonlinear output tracking control problems. Finally, simulations and comparisons are provided on two practical examples to illustrate the validity and effectiveness of the proposed method.

Human Mobility Monitoring in Very Low Resolution Visual Sensor Network

PubMed Central

Bo Bo, Nyan; Deboeverie, Francis; Eldib, Mohamed; Guan, Junzhi; Xie, Xingzhe; Niño, Jorge; Van Haerenborgh, Dirk; Slembrouck, Maarten; Van de Velde, Samuel; Steendam, Heidi; Veelaert, Peter; Kleihorst, Richard; Aghajan, Hamid; Philips, Wilfried

2014-01-01

This paper proposes an automated system for monitoring mobility patterns using a network of very low resolution visual sensors (30 × 30 pixels). The use of very low resolution sensors reduces privacy concern, cost, computation requirement and power consumption. The core of our proposed system is a robust people tracker that uses low resolution videos provided by the visual sensor network. The distributed processing architecture of our tracking system allows all image processing tasks to be done on the digital signal controller in each visual sensor. In this paper, we experimentally show that reliable tracking of people is possible using very low resolution imagery. We also compare the performance of our tracker against a state-of-the-art tracking method and show that our method outperforms. Moreover, the mobility statistics of tracks such as total distance traveled and average speed derived from trajectories are compared with those derived from ground truth given by Ultra-Wide Band sensors. The results of this comparison show that the trajectories from our system are accurate enough to obtain useful mobility statistics. PMID:25375754

Indirect iterative learning control for a discrete visual servo without a camera-robot model.

PubMed

Jiang, Ping; Bamforth, Leon C A; Feng, Zuren; Baruch, John E F; Chen, YangQuan

2007-08-01

This paper presents a discrete learning controller for vision-guided robot trajectory imitation with no prior knowledge of the camera-robot model. A teacher demonstrates a desired movement in front of a camera, and then, the robot is tasked to replay it by repetitive tracking. The imitation procedure is considered as a discrete tracking control problem in the image plane, with an unknown and time-varying image Jacobian matrix. Instead of updating the control signal directly, as is usually done in iterative learning control (ILC), a series of neural networks are used to approximate the unknown Jacobian matrix around every sample point in the demonstrated trajectory, and the time-varying weights of local neural networks are identified through repetitive tracking, i.e., indirect ILC. This makes repetitive segmented training possible, and a segmented training strategy is presented to retain the training trajectories solely within the effective region for neural network approximation. However, a singularity problem may occur if an unmodified neural-network-based Jacobian estimation is used to calculate the robot end-effector velocity. A new weight modification algorithm is proposed which ensures invertibility of the estimation, thus circumventing the problem. Stability is further discussed, and the relationship between the approximation capability of the neural network and the tracking accuracy is obtained. Simulations and experiments are carried out to illustrate the validity of the proposed controller for trajectory imitation of robot manipulators with unknown time-varying Jacobian matrices.

A review of influenza detection and prediction through social networking sites.

PubMed

Alessa, Ali; Faezipour, Miad

2018-02-01

Early prediction of seasonal epidemics such as influenza may reduce their impact in daily lives. Nowadays, the web can be used for surveillance of diseases. Search engines and social networking sites can be used to track trends of different diseases seven to ten days faster than government agencies such as Center of Disease Control and Prevention (CDC). CDC uses the Illness-Like Influenza Surveillance Network (ILINet), which is a program used to monitor Influenza-Like Illness (ILI) sent by thousands of health care providers in order to detect influenza outbreaks. It is a reliable tool, however, it is slow and expensive. For that reason, many studies aim to develop methods that do real time analysis to track ILI using social networking sites. Social media data such as Twitter can be used to predict the spread of flu in the population and can help in getting early warnings. Today, social networking sites (SNS) are used widely by many people to share thoughts and even health status. Therefore, SNS provides an efficient resource for disease surveillance and a good way to communicate to prevent disease outbreaks. The goal of this study is to review existing alternative solutions that track flu outbreak in real time using social networking sites and web blogs. Many studies have shown that social networking sites can be used to conduct real time analysis for better predictions.

A Novel Sensor Selection and Power Allocation Algorithm for Multiple-Target Tracking in an LPI Radar Network

PubMed Central

She, Ji; Wang, Fei; Zhou, Jianjiang

2016-01-01

Radar networks are proven to have numerous advantages over traditional monostatic and bistatic radar. With recent developments, radar networks have become an attractive platform due to their low probability of intercept (LPI) performance for target tracking. In this paper, a joint sensor selection and power allocation algorithm for multiple-target tracking in a radar network based on LPI is proposed. It is found that this algorithm can minimize the total transmitted power of a radar network on the basis of a predetermined mutual information (MI) threshold between the target impulse response and the reflected signal. The MI is required by the radar network system to estimate target parameters, and it can be calculated predictively with the estimation of target state. The optimization problem of sensor selection and power allocation, which contains two variables, is non-convex and it can be solved by separating power allocation problem from sensor selection problem. To be specific, the optimization problem of power allocation can be solved by using the bisection method for each sensor selection scheme. Also, the optimization problem of sensor selection can be solved by a lower complexity algorithm based on the allocated powers. According to the simulation results, it can be found that the proposed algorithm can effectively reduce the total transmitted power of a radar network, which can be conducive to improving LPI performance. PMID:28009819

Online Variational Bayesian Filtering-Based Mobile Target Tracking in Wireless Sensor Networks

PubMed Central

Zhou, Bingpeng; Chen, Qingchun; Li, Tiffany Jing; Xiao, Pei

2014-01-01

The received signal strength (RSS)-based online tracking for a mobile node in wireless sensor networks (WSNs) is investigated in this paper. Firstly, a multi-layer dynamic Bayesian network (MDBN) is introduced to characterize the target mobility with either directional or undirected movement. In particular, it is proposed to employ the Wishart distribution to approximate the time-varying RSS measurement precision's randomness due to the target movement. It is shown that the proposed MDBN offers a more general analysis model via incorporating the underlying statistical information of both the target movement and observations, which can be utilized to improve the online tracking capability by exploiting the Bayesian statistics. Secondly, based on the MDBN model, a mean-field variational Bayesian filtering (VBF) algorithm is developed to realize the online tracking of a mobile target in the presence of nonlinear observations and time-varying RSS precision, wherein the traditional Bayesian filtering scheme cannot be directly employed. Thirdly, a joint optimization between the real-time velocity and its prior expectation is proposed to enable online velocity tracking in the proposed online tacking scheme. Finally, the associated Bayesian Cramer–Rao Lower Bound (BCRLB) analysis and numerical simulations are conducted. Our analysis unveils that, by exploiting the potential state information via the general MDBN model, the proposed VBF algorithm provides a promising solution to the online tracking of a mobile node in WSNs. In addition, it is shown that the final tracking accuracy linearly scales with its expectation when the RSS measurement precision is time-varying. PMID:25393784

"Star Light, Star Bright..."

ERIC Educational Resources Information Center

Moore, Gil; Doop, Skip; Millson, David

1998-01-01

Describes Student-Tracked Atmospheric Research Satellite for Heuristic International Networking Experiment (STARSHINE), which enables students to explore optical astronomy, orbital dynamics, space and atmospheric physics, mathematics and international cooperation by tracking a satellite. (Author)

Experimental Study on the Precise Orbit Determination of the BeiDou Navigation Satellite System

PubMed Central

He, Lina; Ge, Maorong; Wang, Jiexian; Wickert, Jens; Schuh, Harald

2013-01-01

The regional service of the Chinese BeiDou satellite navigation system is now in operation with a constellation including five Geostationary Earth Orbit satellites (GEO), five Inclined Geosynchronous Orbit (IGSO) satellites and four Medium Earth Orbit (MEO) satellites. Besides the standard positioning service with positioning accuracy of about 10 m, both precise relative positioning and precise point positioning are already demonstrated. As is well known, precise orbit and clock determination is essential in enhancing precise positioning services. To improve the satellite orbits of the BeiDou regional system, we concentrate on the impact of the tracking geometry and the involvement of MEOs, and on the effect of integer ambiguity resolution as well. About seven weeks of data collected at the BeiDou Experimental Test Service (BETS) network is employed in this experimental study. Several tracking scenarios are defined, various processing schemata are designed and carried out; and then, the estimates are compared and analyzed in detail. The results show that GEO orbits, especially the along-track component, can be significantly improved by extending the tracking network in China along longitude direction, whereas IGSOs gain more improvement if the tracking network extends in latitude. The involvement of MEOs and ambiguity-fixing also make the orbits better. PMID:23529116

Experimental study on the precise orbit determination of the BeiDou navigation satellite system.

PubMed

He, Lina; Ge, Maorong; Wang, Jiexian; Wickert, Jens; Schuh, Harald

2013-03-01

The regional service of the Chinese BeiDou satellite navigation system is now in operation with a constellation including five Geostationary Earth Orbit satellites (GEO), five Inclined Geosynchronous Orbit (IGSO) satellites and four Medium Earth Orbit (MEO) satellites. Besides the standard positioning service with positioning accuracy of about 10 m, both precise relative positioning and precise point positioning are already demonstrated. As is well known, precise orbit and clock determination is essential in enhancing precise positioning services. To improve the satellite orbits of the BeiDou regional system, we concentrate on the impact of the tracking geometry and the involvement of MEOs, and on the effect of integer ambiguity resolution as well. About seven weeks of data collected at the BeiDou Experimental Test Service (BETS) network is employed in this experimental study. Several tracking scenarios are defined, various processing schemata are designed and carried out; and then, the estimates are compared and analyzed in detail. The results show that GEO orbits, especially the along-track component, can be significantly improved by extending the tracking network in China along longitude direction, whereas IGSOs gain more improvement if the tracking network extends in latitude. The involvement of MEOs and ambiguity-fixing also make the orbits better.

Tracking fin whales in the northeast Pacific Ocean with a seafloor seismic network.

PubMed

Wilcock, William S D

2012-10-01

Ocean bottom seismometer (OBS) networks represent a tool of opportunity to study fin and blue whales. A small OBS network on the Juan de Fuca Ridge in the northeast Pacific Ocean in ~2.3 km of water recorded an extensive data set of 20-Hz fin whale calls. An automated method has been developed to identify arrival times based on instantaneous frequency and amplitude and to locate calls using a grid search even in the presence of a few bad arrival times. When only one whale is calling near the network, tracks can generally be obtained up to distances of ~15 km from the network. When the calls from multiple whales overlap, user supervision is required to identify tracks. The absolute and relative amplitudes of arrivals and their three-component particle motions provide additional constraints on call location but are not useful for extending the distance to which calls can be located. The double-difference method inverts for changes in relative call locations using differences in residuals for pairs of nearby calls recorded on a common station. The method significantly reduces the unsystematic component of the location error, especially when inconsistencies in arrival time observations are minimized by cross-correlation.

Colloid Surface Chemistry Critically Affects Multiple Particle Tracking Measurements of Biomaterials

PubMed Central

Valentine, M. T.; Perlman, Z. E.; Gardel, M. L.; Shin, J. H.; Matsudaira, P.; Mitchison, T. J.; Weitz, D. A.

2004-01-01

Characterization of the properties of complex biomaterials using microrheological techniques has the promise of providing fundamental insights into their biomechanical functions; however, precise interpretations of such measurements are hindered by inadequate characterization of the interactions between tracers and the networks they probe. We here show that colloid surface chemistry can profoundly affect multiple particle tracking measurements of networks of fibrin, entangled F-actin solutions, and networks of cross-linked F-actin. We present a simple protocol to render the surface of colloidal probe particles protein-resistant by grafting short amine-terminated methoxy-poly(ethylene glycol) to the surface of carboxylated microspheres. We demonstrate that these poly(ethylene glycol)-coated tracers adsorb significantly less protein than particles coated with bovine serum albumin or unmodified probe particles. We establish that varying particle surface chemistry selectively tunes the sensitivity of the particles to different physical properties of their microenvironments. Specifically, particles that are weakly bound to a heterogeneous network are sensitive to changes in network stiffness, whereas protein-resistant tracers measure changes in the viscosity of the fluid and in the network microstructure. We demonstrate experimentally that two-particle microrheology analysis significantly reduces differences arising from tracer surface chemistry, indicating that modifications of network properties near the particle do not introduce large-scale heterogeneities. Our results establish that controlling colloid-protein interactions is crucial to the successful application of multiple particle tracking techniques to reconstituted protein networks, cytoplasm, and cells. PMID:15189896

Multi-phenomenology Observation Network Evaluation Tool'' (MONET)

NASA Astrophysics Data System (ADS)

Oltrogge, D.; North, P.; Vallado, D.

2014-09-01

Evaluating overall performance of an SSA "system-of-systems" observational network collecting against thousands of Resident Space Objects (RSO) is very difficult for typical tasking or scheduling-based analysis tools. This is further complicated by networks that have a wide variety of sensor types and phenomena, to include optical, radar and passive RF types, each having unique resource, ops tempo, competing customer and detectability constraints. We present details of the Multi-phenomenology Observation Network Evaluation Tool (MONET), which circumvents these difficulties by assessing the ideal performance of such a network via a digitized supply-vs-demand approach. Cells of each sensors supply time are distributed among RSO targets of interest to determine the average performance of the network against that set of RSO targets. Orbit Determination heuristics are invoked to represent observation quantity and geometry notionally required to obtain the desired orbit estimation quality. To feed this approach, we derive the detectability and collection rate performance of optical, radar and passive RF sensor physical and performance characteristics. We then prioritize the selected RSO targets according to object size, active/inactive status, orbit regime, and/or other considerations. Finally, the OD-derived tracking demands of each RSO of interest are levied against remaining sensor supply until either (a) all sensor time is exhausted; or (b) the list of RSO targets is exhausted. The outputs from MONET include overall network performance metrics delineated by sensor type, objects and orbits tracked, along with likely orbit accuracies which might result from the conglomerate network tracking.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1984-01-01

Tracking and ground-based navigation; communications, spacecraft-ground; station control and system technology; capabilities for new projects; networks consolidation program; and network sustaining are described.

Feasibility study of an integrated optic switching center. [satellite tracking application

NASA Technical Reports Server (NTRS)

1979-01-01

The design of a high data rate switching center for a satellite tracking station is discussed. The feasibility of a switching network using an integrated switching matrix is assessed. The preferred integrated optical switching scheme was found to be an electro-optic Bragg diffraction switch. To ascertain the advantages of the integrated optics switching center, its properties are compared to those of opto-electronic and to electronics switching networks.

A DNA-based molecular motor that can navigate a network of tracks

NASA Astrophysics Data System (ADS)

Wickham, Shelley F. J.; Bath, Jonathan; Katsuda, Yousuke; Endo, Masayuki; Hidaka, Kumi; Sugiyama, Hiroshi; Turberfield, Andrew J.

2012-03-01

Synthetic molecular motors can be fuelled by the hydrolysis or hybridization of DNA. Such motors can move autonomously and programmably, and long-range transport has been observed on linear tracks. It has also been shown that DNA systems can compute. Here, we report a synthetic DNA-based system that integrates long-range transport and information processing. We show that the path of a motor through a network of tracks containing four possible routes can be programmed using instructions that are added externally or carried by the motor itself. When external control is used we find that 87% of the motors follow the correct path, and when internal control is used 71% of the motors follow the correct path. Programmable motion will allow the development of computing networks, molecular systems that can sort and process cargoes according to instructions that they carry, and assembly lines that can be reconfigured dynamically in response to changing demands.

Tracking cohesive subgroups over time in inferred social networks

NASA Astrophysics Data System (ADS)

Chin, Alvin; Chignell, Mark; Wang, Hao

2010-04-01

As a first step in the development of community trackers for large-scale online interaction, this paper shows how cohesive subgroup analysis using the Social Cohesion Analysis of Networks (SCAN; Chin and Chignell 2008) and Data-Intensive Socially Similar Evolving Community Tracker (DISSECT; Chin and Chignell 2010) methods can be applied to the problem of identifying cohesive subgroups and tracking them over time. Three case studies are reported, and the findings are used to evaluate how well the SCAN and DISSECT methods work for different types of data. In the largest of the case studies, variations in temporal cohesiveness are identified across a set of subgroups extracted from the inferred social network. Further modifications to the DISSECT methodology are suggested based on the results obtained. The paper concludes with recommendations concerning further research that would be beneficial in addressing the community tracking problem for online data.

A Non-Intrusive Cyber Physical Social Sensing Solution to People Behavior Tracking: Mechanism, Prototype, and Field Experiments.

PubMed

Jia, Yunjian; Zhou, Zhenyu; Chen, Fei; Duan, Peng; Guo, Zhen; Mumtaz, Shahid

2017-01-13

Tracking people's behaviors is a main category of cyber physical social sensing (CPSS)-related people-centric applications. Most tracking methods utilize camera networks or sensors built into mobile devices such as global positioning system (GPS) and Bluetooth. In this article, we propose a non-intrusive wireless fidelity (Wi-Fi)-based tracking method. To show the feasibility, we target tracking people's access behaviors in Wi-Fi networks, which has drawn a lot of interest from the academy and industry recently. Existing methods used for acquiring access traces either provide very limited visibility into media access control (MAC)-level transmission dynamics or sometimes are inflexible and costly. In this article, we present a passive CPSS system operating in a non-intrusive, flexible, and simplified manner to overcome above limitations. We have implemented the prototype on the off-the-shelf personal computer, and performed real-world deployment experiments. The experimental results show that the method is feasible, and people's access behaviors can be correctly tracked within a one-second delay.

Sensor and tracking data integration into a common operating picture

NASA Astrophysics Data System (ADS)

Bailey, Mark E.

2003-09-01

With rapid technological developments, a new innovative range of possibilities can be actualized in mainstreaming a network with checks and balances to provide sensor and tracking data integration/information to a wider Department of Defense (DoD) audience or group of agencies. As technologies are developed, methods to display the data are required. Multiple diverse tracking devices and sensors need to be displayed on a common operating picture. Sensors and tracking devices are used to monitor an area or object for movement or boundary penetration. Tracking devices in turn determine transit patterns of humans, animals and/or vehicles. In consortium these devices can have dual applications for military requirements and for other general purposes. The DoD Counterdrug Technology Development Program Office (CDTDPO) has designed a system to distribute sensor and tracking data to multiple users in separate agencies. This information can be displayed in whole or in part as to the specific needs of the user. It is with this purpose that the Data Distribution Network (DDN) was created to disseminate information to a collective group or to a select audience.

A Non-Intrusive Cyber Physical Social Sensing Solution to People Behavior Tracking: Mechanism, Prototype, and Field Experiments

PubMed Central

Jia, Yunjian; Zhou, Zhenyu; Chen, Fei; Duan, Peng; Guo, Zhen; Mumtaz, Shahid

2017-01-01

Tracking people’s behaviors is a main category of cyber physical social sensing (CPSS)-related people-centric applications. Most tracking methods utilize camera networks or sensors built into mobile devices such as global positioning system (GPS) and Bluetooth. In this article, we propose a non-intrusive wireless fidelity (Wi-Fi)-based tracking method. To show the feasibility, we target tracking people’s access behaviors in Wi-Fi networks, which has drawn a lot of interest from the academy and industry recently. Existing methods used for acquiring access traces either provide very limited visibility into media access control (MAC)-level transmission dynamics or sometimes are inflexible and costly. In this article, we present a passive CPSS system operating in a non-intrusive, flexible, and simplified manner to overcome above limitations. We have implemented the prototype on the off-the-shelf personal computer, and performed real-world deployment experiments. The experimental results show that the method is feasible, and people’s access behaviors can be correctly tracked within a one-second delay. PMID:28098772

Distributed Peer-to-Peer Target Tracking in Wireless Sensor Networks

PubMed Central

Wang, Xue; Wang, Sheng; Bi, Dao-Wei; Ma, Jun-Jie

2007-01-01

Target tracking is usually a challenging application for wireless sensor networks (WSNs) because it is always computation-intensive and requires real-time processing. This paper proposes a practical target tracking system based on the auto regressive moving average (ARMA) model in a distributed peer-to-peer (P2P) signal processing framework. In the proposed framework, wireless sensor nodes act as peers that perform target detection, feature extraction, classification and tracking, whereas target localization requires the collaboration between wireless sensor nodes for improving the accuracy and robustness. For carrying out target tracking under the constraints imposed by the limited capabilities of the wireless sensor nodes, some practically feasible algorithms, such as the ARMA model and the 2-D integer lifting wavelet transform, are adopted in single wireless sensor nodes due to their outstanding performance and light computational burden. Furthermore, a progressive multi-view localization algorithm is proposed in distributed P2P signal processing framework considering the tradeoff between the accuracy and energy consumption. Finally, a real world target tracking experiment is illustrated. Results from experimental implementations have demonstrated that the proposed target tracking system based on a distributed P2P signal processing framework can make efficient use of scarce energy and communication resources and achieve target tracking successfully.

Tracks of a Giant

NASA Image and Video Library

2010-08-25

The giant, 70-meter-wide antenna at NASA Deep Space Network complex in Goldstone, Calif., tracks a spacecraft on Nov. 17, 2009. This antenna, officially known as Deep Space Station 14, is also nicknamed the Mars antenna.

Distributed cooperative regulation for multiagent systems and its applications to power systems: a survey.

PubMed

Hu, Jianqiang; Li, Yaping; Yong, Taiyou; Cao, Jinde; Yu, Jie; Mao, Wenbo

2014-01-01

Cooperative regulation of multiagent systems has become an active research area in the past decade. This paper reviews some recent progress in distributed coordination control for leader-following multiagent systems and its applications in power system and mainly focuses on the cooperative tracking control in terms of consensus tracking control and containment tracking control. Next, methods on how to rank the network nodes are summarized for undirected/directed network, based on which one can determine which follower should be connected to leaders such that partial followers can perceive leaders' information. Furthermore, we present a survey of the most relevant scientific studies investigating the regulation and optimization problems in power systems based on distributed strategies. Finally, some potential applications in the frequency tracking regulation of smart grids are discussed at the end of the paper.

Distributed Cooperative Regulation for Multiagent Systems and Its Applications to Power Systems: A Survey

PubMed Central

Li, Yaping; Yong, Taiyou; Yu, Jie; Mao, Wenbo

2014-01-01

Cooperative regulation of multiagent systems has become an active research area in the past decade. This paper reviews some recent progress in distributed coordination control for leader-following multiagent systems and its applications in power system and mainly focuses on the cooperative tracking control in terms of consensus tracking control and containment tracking control. Next, methods on how to rank the network nodes are summarized for undirected/directed network, based on which one can determine which follower should be connected to leaders such that partial followers can perceive leaders' information. Furthermore, we present a survey of the most relevant scientific studies investigating the regulation and optimization problems in power systems based on distributed strategies. Finally, some potential applications in the frequency tracking regulation of smart grids are discussed at the end of the paper. PMID:25243199

A Radar-Enabled Collaborative Sensor Network Integrating COTS Technology for Surveillance and Tracking

PubMed Central

Kozma, Robert; Wang, Lan; Iftekharuddin, Khan; McCracken, Ernest; Khan, Muhammad; Islam, Khandakar; Bhurtel, Sushil R.; Demirer, R. Murat

2012-01-01

The feasibility of using Commercial Off-The-Shelf (COTS) sensor nodes is studied in a distributed network, aiming at dynamic surveillance and tracking of ground targets. Data acquisition by low-cost (<$50 US) miniature low-power radar through a wireless mote is described. We demonstrate the detection, ranging and velocity estimation, classification and tracking capabilities of the mini-radar, and compare results to simulations and manual measurements. Furthermore, we supplement the radar output with other sensor modalities, such as acoustic and vibration sensors. This method provides innovative solutions for detecting, identifying, and tracking vehicles and dismounts over a wide area in noisy conditions. This study presents a step towards distributed intelligent decision support and demonstrates effectiveness of small cheap sensors, which can complement advanced technologies in certain real-life scenarios. PMID:22438713

Network Information System

DOE Office of Scientific and Technical Information (OSTI.GOV)

1996-05-01

The Network Information System (NWIS) was initially implemented in May 1996 as a system in which computing devices could be recorded so that unique names could be generated for each device. Since then the system has grown to be an enterprise wide information system which is integrated with other systems to provide the seamless flow of data through the enterprise. The system Iracks data for two main entities: people and computing devices. The following are the type of functions performed by NWIS for these two entities: People Provides source information to the enterprise person data repository for select contractors andmore » visitors Generates and tracks unique usernames and Unix user IDs for every individual granted cyber access Tracks accounts for centrally managed computing resources, and monitors and controls the reauthorization of the accounts in accordance with the DOE mandated interval Computing Devices Generates unique names for all computing devices registered in the system Tracks the following information for each computing device: manufacturer, make, model, Sandia property number, vendor serial number, operating system and operating system version, owner, device location, amount of memory, amount of disk space, and level of support provided for the machine Tracks the hardware address for network cards Tracks the P address registered to computing devices along with the canonical and alias names for each address Updates the Dynamic Domain Name Service (DDNS) for canonical and alias names Creates the configuration files for DHCP to control the DHCP ranges and allow access to only properly registered computers Tracks and monitors classified security plans for stand-alone computers Tracks the configuration requirements used to setup the machine Tracks the roles people have on machines (system administrator, administrative access, user, etc...) Allows systems administrators to track changes made on the machine (both hardware and software) Generates an adjustment history of changes on selected fields« less

Impacts of Climate Change on Operation of the US Rail Network

EPA Science Inventory

The rail network in the US is the largest network within any single country at 140,000 miles of Class 1 tracks. The network is predominantly focused on freight traffic with the exception of key passenger corridors along the eastern seaboard and in the upper Midwest. This extens...

SENSITIVITY ANALYSIS OF THE MULTI-LAYER MODEL USED IN THE CLEAN AIR STATUS AND TRENDS NETWORK (CASTNET)

EPA Science Inventory

The U.S. Environmental Protection Agency (EPA) established the Clean Air Status and Trends Network (CASTNET) and its predecessor, the National Dry Deposition Network (NDDN), as national air quality and meteorological monitoring networks. The purpose of CASTNET is to track the pr...

Information Weighted Consensus for Distributed Estimation in Vision Networks

ERIC Educational Resources Information Center

Kamal, Ahmed Tashrif

2013-01-01

Due to their high fault-tolerance, ease of installation and scalability to large networks, distributed algorithms have recently gained immense popularity in the sensor networks community, especially in computer vision. Multi-target tracking in a camera network is one of the fundamental problems in this domain. Distributed estimation algorithms…

The deep space network, Volume 11

NASA Technical Reports Server (NTRS)

1972-01-01

Deep Space Network progress in flight project support, Tracking and Data Acquisition research and technology, network engineering, hardware and software implementation, and operations are presented. Material is presented in each of the following categories: description of DSN; mission support; radio science; support research and technology; network engineering and implementation; and operations and facilities.

Optimization of RFID network planning using Zigbee and WSN

NASA Astrophysics Data System (ADS)

Hasnan, Khalid; Ahmed, Aftab; Badrul-aisham, Bakhsh, Qadir

2015-05-01

Everyone wants to be ease in their life. Radio frequency identification (RFID) wireless technology is used to make our life easier. RFID technology increases productivity, accuracy and convenience in delivery of service in supply chain. It is used for various applications such as preventing theft of automobiles, tolls collection without stopping, no checkout lines at grocery stores, managing traffic, hospital management, corporate campuses and airports, mobile asset tracking, warehousing, tracking library books, and to track a wealth of assets in supply chain management. Efficiency of RFID can be enhanced by integrating with wireless sensor network (WSN), zigbee mesh network and internet of things (IOT). The proposed system is used for identifying, sensing and real-time locating system (RTLS) of items in an indoor heterogeneous region. The system gives real-time richer information of object's characteristics, location and their environmental parameters like temperature, noise and humidity etc. RTLS reduce human error, optimize inventory management, increase productivity and information accuracy at indoor heterogeneous network. The power consumption and the data transmission rate of the system can be minimized by using low power hardware design.

An Alternative Wearable Tracking System Based on a Low-Power Wide-Area Network.

PubMed

Fernández-Garcia, Raul; Gil, Ignacio

2017-03-14

This work presents an alternative wearable tracking system based on a low-power wide area network. A complete GPS receiver was integrated with a textile substrate, and the latitude and longitude coordinates were sent to the cloud by means of the SIM-less SIGFOX network. To send the coordinates over SIGFOX protocol, a specific codification algorithm was used and a customized UHF antenna on jeans fabric was designed, simulated and tested. Moreover, to guarantee the compliance to international regulations for human body exposure to electromagnetic radiation, the electromagnetic specific absorption rate of this antenna was analyzed. A specific remote server was developed to decode the latitude and longitude coordinates. Once the coordinates have been decoded, the remote server sends this information to the open source data viewer SENTILO to show the location of the sensor node in a map. The functionality of this system has been demonstrated experimentally. The results guarantee the utility and wearability of the proposed tracking system for the development of sensor nodes and point out that it can be a low cost alternative to other commercial products based on GSM networks.

Performance of a Protected Wireless Sensor Network in a Fire. Analysis of Fire Spread and Data Transmission

PubMed Central

Antoine-Santoni, Thierry; Santucci, Jean-François; de Gentili, Emmanuelle; Silvani, Xavier; Morandini, Frederic

2009-01-01

The paper deals with a Wireless Sensor Network (WSN) as a reliable solution for capturing the kinematics of a fire front spreading over a fuel bed. To provide reliable information in fire studies and support fire fighting strategies, a Wireless Sensor Network must be able to perform three sequential actions: 1) sensing thermal data in the open as the gas temperature; 2) detecting a fire i.e., the spatial position of a flame; 3) tracking the fire spread during its spatial and temporal evolution. One of the great challenges in performing fire front tracking with a WSN is to avoid the destruction of motes by the fire. This paper therefore shows the performance of Wireless Sensor Network when the motes are protected with a thermal insulation dedicated to track a fire spreading across vegetative fuels on a field scale. The resulting experimental WSN is then used in series of wildfire experiments performed in the open in vegetation areas ranging in size from 50 to 1,000 m2. PMID:22454563

Performance of a protected wireless sensor network in a fire. Analysis of fire spread and data transmission.

PubMed

Antoine-Santoni, Thierry; Santucci, Jean-François; de Gentili, Emmanuelle; Silvani, Xavier; Morandini, Frederic

2009-01-01

The paper deals with a Wireless Sensor Network (WSN) as a reliable solution for capturing the kinematics of a fire front spreading over a fuel bed. To provide reliable information in fire studies and support fire fighting strategies, a Wireless Sensor Network must be able to perform three sequential actions: 1) sensing thermal data in the open as the gas temperature; 2) detecting a fire i.e., the spatial position of a flame; 3) tracking the fire spread during its spatial and temporal evolution. One of the great challenges in performing fire front tracking with a WSN is to avoid the destruction of motes by the fire. This paper therefore shows the performance of Wireless Sensor Network when the motes are protected with a thermal insulation dedicated to track a fire spreading across vegetative fuels on a field scale. The resulting experimental WSN is then used in series of wildfire experiments performed in the open in vegetation areas ranging in size from 50 to 1,000 m(2).

Robust visual tracking based on deep convolutional neural networks and kernelized correlation filters

NASA Astrophysics Data System (ADS)

Yang, Hua; Zhong, Donghong; Liu, Chenyi; Song, Kaiyou; Yin, Zhouping

2018-03-01

Object tracking is still a challenging problem in computer vision, as it entails learning an effective model to account for appearance changes caused by occlusion, out of view, plane rotation, scale change, and background clutter. This paper proposes a robust visual tracking algorithm called deep convolutional neural network (DCNNCT) to simultaneously address these challenges. The proposed DCNNCT algorithm utilizes a DCNN to extract the image feature of a tracked target, and the full range of information regarding each convolutional layer is used to express the image feature. Subsequently, the kernelized correlation filters (CF) in each convolutional layer are adaptively learned, the correlation response maps of that are combined to estimate the location of the tracked target. To avoid the case of tracking failure, an online random ferns classifier is employed to redetect the tracked target, and a dual-threshold scheme is used to obtain the final target location by comparing the tracking result with the detection result. Finally, the change in scale of the target is determined by building scale pyramids and training a CF. Extensive experiments demonstrate that the proposed algorithm is effective at tracking, especially when evaluated using an index called the overlap rate. The DCNNCT algorithm is also highly competitive in terms of robustness with respect to state-of-the-art trackers in various challenging scenarios.

Open solutions to distributed control in ground tracking stations

NASA Technical Reports Server (NTRS)

Heuser, William Randy

1994-01-01

The advent of high speed local area networks has made it possible to interconnect small, powerful computers to function together as a single large computer. Today, distributed computer systems are the new paradigm for large scale computing systems. However, the communications provided by the local area network is only one part of the solution. The services and protocols used by the application programs to communicate across the network are as indispensable as the local area network. And the selection of services and protocols that do not match the system requirements will limit the capabilities, performance, and expansion of the system. Proprietary solutions are available but are usually limited to a select set of equipment. However, there are two solutions based on 'open' standards. The question that must be answered is 'which one is the best one for my job?' This paper examines a model for tracking stations and their requirements for interprocessor communications in the next century. The model and requirements are matched with the model and services provided by the five different software architectures and supporting protocol solutions. Several key services are examined in detail to determine which services and protocols most closely match the requirements for the tracking station environment. The study reveals that the protocols are tailored to the problem domains for which they were originally designed. Further, the study reveals that the process control model is the closest match to the tracking station model.

Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision.

PubMed

Zhong, Bineng; Pan, Shengnan; Zhang, Hongbo; Wang, Tian; Du, Jixiang; Chen, Duansheng; Cao, Liujuan

2016-01-01

In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data. Finally, to alleviate the tracker drifting problem caused by model updating, we jointly consider three different types of positive samples. Extensive experiments validate the robustness and effectiveness of the proposed method.

Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision

PubMed Central

Pan, Shengnan; Zhang, Hongbo; Wang, Tian; Du, Jixiang; Chen, Duansheng; Cao, Liujuan

2016-01-01

In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data. Finally, to alleviate the tracker drifting problem caused by model updating, we jointly consider three different types of positive samples. Extensive experiments validate the robustness and effectiveness of the proposed method. PMID:27847827

Event-triggered Kalman-consensus filter for two-target tracking sensor networks.

PubMed

Su, Housheng; Li, Zhenghao; Ye, Yanyan

2017-11-01

This paper is concerned with the problem of event-triggered Kalman-consensus filter for two-target tracking sensor networks. According to the event-triggered protocol and the mean-square analysis, a suboptimal Kalman gain matrix is derived and a suboptimal event-triggered distributed filter is obtained. Based on the Kalman-consensus filter protocol, all sensors which only depend on its neighbors' information can track their corresponding targets. Furthermore, utilizing Lyapunov method and matrix theory, some sufficient conditions are presented for ensuring the stability of the system. Finally, a simulation example is presented to verify the effectiveness of the proposed event-triggered protocol. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Dynamic Creation of Social Networks for Syndromic Surveillance Using Information Fusion

NASA Astrophysics Data System (ADS)

Holsopple, Jared; Yang, Shanchieh; Sudit, Moises; Stotz, Adam

To enhance the effectiveness of health care, many medical institutions have started transitioning to electronic health and medical records and sharing these records between institutions. The large amount of complex and diverse data makes it difficult to identify and track relationships and trends, such as disease outbreaks, from the data points. INFERD: Information Fusion Engine for Real-Time Decision-Making is an information fusion tool that dynamically correlates and tracks event progressions. This paper presents a methodology that utilizes the efficient and flexible structure of INFERD to create social networks representing progressions of disease outbreaks. Individual symptoms are treated as features allowing multiple hypothesis being tracked and analyzed for effective and comprehensive syndromic surveillance.

Distributed Communications Resource Management for Tracking and Surveillance Networks

DTIC Science & Technology

2005-08-01

Principles of Economics , Ludwig von Mises Institute, Auburn, AL, 2004. 13. J. Wang, L. Li, S. H. Low and J. C. Doyle, “Cross-layer Optimization in TCP/IP Networks,” IEEE/ACM Trans. on Networking, 2005, to appear.

Tracking and data system support for the Pioneer project. Pioneers 6-9, extended missions: 1 July 1972 - 1 July 1973, volume 12

NASA Technical Reports Server (NTRS)

Miller, R. B.

1974-01-01

The Tracking and Data System supported the deep space phases of the Pioneer 6, 7, 8, and 9 missions, with two spacecraft in an inward trajectory and two spacecraft in an outward trajectory from the earth in heliocentric orbits. During the period of this report, scientific instruments aboard each of the spacecraft continued to register information relative to interplanetary particles and fields, and radiometric data generated by the network continued to contribute to knowledge of the celestial mechanics of the solar system. In addition, to network support activity detail, network performance and special support activities are covered.

Tracking and data system support for the pioneer project. Volume 11 Pioneers 6-9. Extended missions: 1 July 1971 - 1 July 1973

NASA Technical Reports Server (NTRS)

Renzetti, N. A.; Siegmeth, A. J.

1973-01-01

The Tracking and Data System supported the deep space phases of the Pioneer 6, 7, 8, and 9 missions, with two spacecraft in an inward trajectory and two spacecraft in an outward trajectory from the earth in heliocentric orbits. Scientific instruments aboard each of the spacecraft continued to register information relative to interplanetary particles and fields, and radio metric data generated by the network continued to improve our knowledge of the celestial mechanics of the solar system. In addition to network support activity detail, network performance and special support activities are covered.

Simulation and Modeling of a New Medium Access Control Scheme for Multi-Beam Directional Networking

DTIC Science & Technology

2017-03-03

of these packets, it waits until the end of the transmit time and then responds with its own hello packet, containing its own location, as well as...own hello packet. Location Tracking Another important feature is location tracking. Due to node mobility, it is vital that each node tracks the

Network Level Association and Fusion of Kinematic and Attribute Information

DTIC Science & Technology

2010-12-15

L. Svensson, ``More Ways to Track Closely-Spaced Targets Than You Wanted To Know or Tips For Not Pissing Off the Radar Operator So Much That He Turns...To Know or Tips For Not Pissing Off the Radar Operator So Much That He Turns Off the Track Display”, Proc. ONR-GTRI Workshop on Tracking, Santa Barbara

Data analysis-based autonomic bandwidth adjustment in software defined multi-vendor optical transport networks.

PubMed

Li, Yajie; Zhao, Yongli; Zhang, Jie; Yu, Xiaosong; Jing, Ruiquan

2017-11-27

Network operators generally provide dedicated lightpaths for customers to meet the demand for high-quality transmission. Considering the variation of traffic load, customers usually rent peak bandwidth that exceeds the practical average traffic requirement. In this case, bandwidth provisioning is unmetered and customers have to pay according to peak bandwidth. Supposing that network operators could keep track of traffic load and allocate bandwidth dynamically, bandwidth can be provided as a metered service and customers would pay for the bandwidth that they actually use. To achieve cost-effective bandwidth provisioning, this paper proposes an autonomic bandwidth adjustment scheme based on data analysis of traffic load. The scheme is implemented in a software defined networking (SDN) controller and is demonstrated in the field trial of multi-vendor optical transport networks. The field trial shows that the proposed scheme can track traffic load and realize autonomic bandwidth adjustment. In addition, a simulation experiment is conducted to evaluate the performance of the proposed scheme. We also investigate the impact of different parameters on autonomic bandwidth adjustment. Simulation results show that the step size and adjustment period have significant influences on bandwidth savings and packet loss. A small value of step size and adjustment period can bring more benefits by tracking traffic variation with high accuracy. For network operators, the scheme can serve as technical support of realizing bandwidth as metered service in the future.

On-Line Tracking Controller for Brushless DC Motor Drives Using Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Rubaai, Ahmed

1996-01-01

A real-time control architecture is developed for time-varying nonlinear brushless dc motors operating in a high performance drives environment. The developed control architecture possesses the capabilities of simultaneous on-line identification and control. The dynamics of the motor are modeled on-line and controlled using an artificial neural network, as the system runs. The control architecture combines the experience and dependability of adaptive tracking systems with potential and promise of the neural computing technology. The sensitivity of real-time controller to parametric changes that occur during training is investigated. Such changes are usually manifested by rapid changes in the load of the brushless motor drives. This sudden change in the external load is simulated for the sigmoidal and sinusoidal reference tracks. The ability of the neuro-controller to maintain reasonable tracking accuracy in the presence of external noise is also verified for a number of desired reference trajectories.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1988-01-01

Deep Space Network and Systems topics addressed include: tracking and ground-base navigation; communications, spacecraft-ground; station control and system technology; capabilities for existing projects; and network upgrading and sustaining.

Mobile robotic sensors for perimeter detection and tracking.

PubMed

Clark, Justin; Fierro, Rafael

2007-02-01

Mobile robot/sensor networks have emerged as tools for environmental monitoring, search and rescue, exploration and mapping, evaluation of civil infrastructure, and military operations. These networks consist of many sensors each equipped with embedded processors, wireless communication, and motion capabilities. This paper describes a cooperative mobile robot network capable of detecting and tracking a perimeter defined by a certain substance (e.g., a chemical spill) in the environment. Specifically, the contributions of this paper are twofold: (i) a library of simple reactive motion control algorithms and (ii) a coordination mechanism for effectively carrying out perimeter-sensing missions. The decentralized nature of the methodology implemented could potentially allow the network to scale to many sensors and to reconfigure when adding/deleting sensors. Extensive simulation results and experiments verify the validity of the proposed cooperative control scheme.

Variable Neural Adaptive Robust Control: A Switched System Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lian, Jianming; Hu, Jianghai; Zak, Stanislaw H.

2015-05-01

Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multi-input multi-output uncertain systems. The controllers incorporate a variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. The variable-structure RBF network solves the problem of structure determination associated with fixed-structure RBF networks. It can determine the network structure on-line dynamically by adding or removing radial basis functions according to the tracking performance. The structure variation is taken into account in the stability analysis of the closed-loop system using a switched system approach with the aid of the piecewisemore » quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.« less

The Laser Communications Relay and the Path to the Next Generation Near Earth Relay

NASA Technical Reports Server (NTRS)

Israel, David J.

2015-01-01

NASA Goddard Space Flight Center is currently developing the Laser Communications Relay Demonstration (LCRD) as a Path to the Next Generation Near Earth Space Communication Network. The current NASA Space Network or Tracking and Data Relay Satellite System is comprised of a constellation of Tracking and Data Relay Satellites (TDRS) in geosynchronous orbit and associated ground stations and operation centers. NASA is currently targeting a next generation of relay capability on orbit in the 2025 timeframe.

77 FR 72335 - Proposed Collection; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-05

... computer networks, systems, or databases. The records contain the individual's name; social security number... control and track access to DLA-controlled networks, computer systems, and databases. The records may also...

The deep space network, volume 15

NASA Technical Reports Server (NTRS)

1973-01-01

The DSN progress is reported in flight project support, TDA research and technology, network engineering, hardware and software implementation, and operations. Topics discussed include: DSN functions and facilities, planetary flight projects, tracking and ground-based navigation, communications, data processing, network control system, and deep space stations.

The Deep Space Network, volume 39

NASA Technical Reports Server (NTRS)

1977-01-01

The functions, facilities, and capabilities of the Deep Space Network and its support of the Pioneer, Helios, and Viking missions are described. Progress in tracking and data acquisition research and technology, network engineering and modifications, as well as hardware and software implementation and operations are reported.

Evaluation of Data Replacement Strategies for CASTNET Dry Deposition Modeling

EPA Science Inventory

The U.S. Environmental Protection Agency (EPA) established the Clean Air Status and Trends Network (CASTNET) and its predecessor, the National Dry Deposition Network (NDDN), as national air quality and meteorological monitoring networks. The purpose of CASTNET is to track the pr...

The telecommunications and data acquisition report

NASA Technical Reports Server (NTRS)

Renzetti, N. A. (Editor)

1981-01-01

Developments in Earth-based ratio technology as applied to the Deep Space Network are reported. Topics include ratio astronomy and spacecraft tracking networks. Telemetric methods and instrumentation are described. Station control and system technology for space communication is discussed. Special emphasis is placed on network data processing.

The deep space network, volume 16

NASA Technical Reports Server (NTRS)

1973-01-01

The objectives, functions, and organization of the DSN are summarized, and the instrumentation facility, ground communication facility, and the network control system are described. The requirements for supporting planetary flight projects are discussed along with the research and technology for tracking, navigation, network control, and data processing.

Implementation of high precision optical and radiometric LRO tracking data in the orbit determination to supplement the baseline S-band tracking

NASA Astrophysics Data System (ADS)

Mao, D.; Torrence, M. H.; Mazarico, E.; Neumann, G. A.; Smith, D. E.; Zuber, M. T.

2016-12-01

LRO has been in a polar lunar orbit for 7 year since it was launched in June 2009. Seven instruments are onboard LRO to perform a global and detailed geophysical, geological and geochemical mapping of the Moon, some of which have very high spatial resolution. To take full advantage of the high resolution LRO datasets from these instruments, the spacecraft orbit must be reconstructed precisely. The baseline LRO tracking was the NASA's White Sands station in New Mexico and a commercial network, the Universal Space Network (USN), providing up to 20 hours per day of almost continuous S-band radio frequency link to LRO. The USN stations produce S-band range data with a 0.4 m precision and Doppler data with a 0.8 mm/s precision. Using the S-band tracking data together with the high-resolution gravity field model from the GRAIL mission, definitive LRO orbit solutions are obtained with an accuracy of 10 m in total position and 0.5 m radially. Confirmed by the 0.50-m high-resolution NAC images from the LROC team, these orbits well represent the LRO orbit "truth". In addition to the S-band data, one-way Laser Ranging (LR) to LRO provides a unique LRO optical tracking dataset over 5 years, from June 2009 to September 2014. Ten international satellite laser ranging stations contributed over 4000 hours LR data with the 0.05 - 0.10 m normal point precision. Another set of high precision LRO tracking data is provided by the Deep Space Network (DSN), which produces radiometric tracking data more precise than the USN S-band data. In the last two years of the LRO mission, the temporal coverage of the USN data has decreased significantly. We show that LR and DSN data can be a good supplement to the baseline tracking data for the orbit reconstruction.

Occlusion handling framework for tracking in smart camera networks by per-target assistance task assignment

NASA Astrophysics Data System (ADS)

Bo, Nyan Bo; Deboeverie, Francis; Veelaert, Peter; Philips, Wilfried

2017-09-01

Occlusion is one of the most difficult challenges in the area of visual tracking. We propose an occlusion handling framework to improve the performance of local tracking in a smart camera view in a multicamera network. We formulate an extensible energy function to quantify the quality of a camera's observation of a particular target by taking into account both person-person and object-person occlusion. Using this energy function, a smart camera assesses the quality of observations over all targets being tracked. When it cannot adequately observe of a target, a smart camera estimates the quality of observation of the target from view points of other assisting cameras. If a camera with better observation of the target is found, the tracking task of the target is carried out with the assistance of that camera. In our framework, only positions of persons being tracked are exchanged between smart cameras. Thus, communication bandwidth requirement is very low. Performance evaluation of our method on challenging video sequences with frequent and severe occlusions shows that the accuracy of a baseline tracker is considerably improved. We also report the performance comparison to the state-of-the-art trackers in which our method outperforms.

Feature Extraction for Track Section Status Classification Based on UGW Signals

PubMed Central

Yang, Yuan; Shi, Lin

2018-01-01

Track status classification is essential for the stability and safety of railway operations nowadays, when railway networks are becoming more and more complex and broad. In this situation, monitoring systems are already a key element in applications dedicated to evaluating the status of a certain track section, often determining whether it is free or occupied by a train. Different technologies have already been involved in the design of monitoring systems, including ultrasonic guided waves (UGW). This work proposes the use of the UGW signals captured by a track monitoring system to extract the features that are relevant for determining the corresponding track section status. For that purpose, three features of UGW signals have been considered: the root mean square value, the energy, and the main frequency components. Experimental results successfully validated how these features can be used to classify the track section status into free, occupied and broken. Furthermore, spatial and temporal dependencies among these features were analysed in order to show how they can improve the final classification performance. Finally, a preliminary high-level classification system based on deep learning networks has been envisaged for future works. PMID:29673156

Phase accumulation tracking algorithm for effective index retrieval of fishnet metamaterials and other resonant guided wave networks

NASA Astrophysics Data System (ADS)

Feigenbaum, Eyal; Hiszpanski, Anna M.

2017-07-01

A phase accumulation tracking (PAT) algorithm is proposed and demonstrated for the retrieval of the effective index of fishnet metamaterials (FMMs) in order to avoid the multi-branch uncertainty problem. This algorithm tracks the phase and amplitude of the dominant propagation mode across the FMM slab. The suggested PAT algorithm applies to resonant guided wave networks having only one mode that carries the light between the two slab ends, where the FMM is one example of this metamaterials sub-class. The effective index is a net effect of positive and negative accumulated phase in the alternating FMM metal and dielectric layers, with a negative effective index occurring when negative phase accumulation dominates.

Tracking and data relay satellite system - NASA's new spacecraft data acquisition system

NASA Technical Reports Server (NTRS)

Schneider, W. C.; Garman, A. A.

1979-01-01

This paper describes NASA's new spacecraft acquisition system provided by the Tracking and Data Relay Satellite System (TDRSS). Four satellites in geostationary orbit and a ground terminal will provide complete tracking, telemetry, and command service for all of NASA's orbital satellites below a 12,000 km altitude. Western Union will lease the system, operate the ground terminal and provide operational satellite control. NASA's network control center will be the focal point for scheduling user services and controlling the interface between TDRSS and the NASA communications network, project control centers, and data processing. TDRSS single access user spacecraft data systems will be designed for time shared data relay support, and reimbursement policy and rate structure for non-NASA users are being developed.

A new role for primary care teams in the United States after “Obamacare:” Track and improve health insurance coverage rates

PubMed Central

DeVoe, Jennifer; Angier, Heather; Hoopes, Megan; Gold, Rachel

2017-01-01

Maintaining continuous health insurance coverage is important. With recent expansions in access to coverage in the United States after “Obamacare,” primary care teams have a new role in helping to track and improve coverage rates and to provide outreach to patients. We describe efforts to longitudinally track health insurance rates using data from the electronic health record (EHR) of a primary care network and to use these data to support practice-based insurance outreach and assistance. Although we highlight a few examples from one network, we believe there is great potential for doing this type of work in a broad range of family medicine and community health clinics that provide continuity of care. By partnering with researchers through practice-based research networks and other similar collaboratives, primary care practices can greatly expand the use of EHR data and EHR-based tools targeting improvements in health insurance and quality health care. PMID:28966926

A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications.

PubMed

Gharghan, Sadik Kamel; Nordin, Rosdiadee; Ismail, Mahamod

2016-08-06

In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS was considered, whereas in the second approach the ANN was hybridized individually with three optimization algorithms, namely Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Backtracking Search Algorithm (BSA). The results revealed that the hybrid GSA-ANN outperforms the other methods adopted in this paper in terms of accuracy localization and distance estimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of 0.02 m and 0.2 m for outdoor and indoor velodromes, respectively.

A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications

PubMed Central

Gharghan, Sadik Kamel; Nordin, Rosdiadee; Ismail, Mahamod

2016-01-01

In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS was considered, whereas in the second approach the ANN was hybridized individually with three optimization algorithms, namely Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Backtracking Search Algorithm (BSA). The results revealed that the hybrid GSA-ANN outperforms the other methods adopted in this paper in terms of accuracy localization and distance estimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of 0.02 m and 0.2 m for outdoor and indoor velodromes, respectively. PMID:27509495

Improved relocatable over-the-horizon radar detection and tracking using the maximum likelihood adaptive neural system algorithm

NASA Astrophysics Data System (ADS)

Perlovsky, Leonid I.; Webb, Virgil H.; Bradley, Scott R.; Hansen, Christopher A.

1998-07-01

An advanced detection and tracking system is being developed for the U.S. Navy's Relocatable Over-the-Horizon Radar (ROTHR) to provide improved tracking performance against small aircraft typically used in drug-smuggling activities. The development is based on the Maximum Likelihood Adaptive Neural System (MLANS), a model-based neural network that combines advantages of neural network and model-based algorithmic approaches. The objective of the MLANS tracker development effort is to address user requirements for increased detection and tracking capability in clutter and improved track position, heading, and speed accuracy. The MLANS tracker is expected to outperform other approaches to detection and tracking for the following reasons. It incorporates adaptive internal models of target return signals, target tracks and maneuvers, and clutter signals, which leads to concurrent clutter suppression, detection, and tracking (track-before-detect). It is not combinatorial and thus does not require any thresholding or peak picking and can track in low signal-to-noise conditions. It incorporates superresolution spectrum estimation techniques exceeding the performance of conventional maximum likelihood and maximum entropy methods. The unique spectrum estimation method is based on the Einsteinian interpretation of the ROTHR received energy spectrum as a probability density of signal frequency. The MLANS neural architecture and learning mechanism are founded on spectrum models and maximization of the "Einsteinian" likelihood, allowing knowledge of the physical behavior of both targets and clutter to be injected into the tracker algorithms. The paper describes the addressed requirements and expected improvements, theoretical foundations, engineering methodology, and results of the development effort to date.

Multi-Object Tracking with Correlation Filter for Autonomous Vehicle.

PubMed

Zhao, Dawei; Fu, Hao; Xiao, Liang; Wu, Tao; Dai, Bin

2018-06-22

Multi-object tracking is a crucial problem for autonomous vehicle. Most state-of-the-art approaches adopt the tracking-by-detection strategy, which is a two-step procedure consisting of the detection module and the tracking module. In this paper, we improve both steps. We improve the detection module by incorporating the temporal information, which is beneficial for detecting small objects. For the tracking module, we propose a novel compressed deep Convolutional Neural Network (CNN) feature based Correlation Filter tracker. By carefully integrating these two modules, the proposed multi-object tracking approach has the ability of re-identification (ReID) once the tracked object gets lost. Extensive experiments were performed on the KITTI and MOT2015 tracking benchmarks. Results indicate that our approach outperforms most state-of-the-art tracking approaches.

A performance study of unmanned aerial vehicle-based sensor networks under cyber attack

NASA Astrophysics Data System (ADS)

Puchaty, Ethan M.

In UAV-based sensor networks, an emerging area of interest is the performance of these networks under cyber attack. This study seeks to evaluate the performance trade-offs from a System-of-Systems (SoS) perspective between various UAV communications architecture options in the context two missions: tracking ballistic missiles and tracking insurgents. An agent-based discrete event simulation is used to model a sensor communication network consisting of UAVs, military communications satellites, ground relay stations, and a mission control center. Network susceptibility to cyber attack is modeled with probabilistic failures and induced data variability, with performance metrics focusing on information availability, latency, and trustworthiness. Results demonstrated that using UAVs as routers increased network availability with a minimal latency penalty and communications satellite networks were best for long distance operations. Redundancy in the number of links between communication nodes helped mitigate cyber-caused link failures and add robustness in cases of induced data variability by an adversary. However, when failures were not independent, redundancy and UAV routing were detrimental in some cases to network performance. Sensitivity studies indicated that long cyber-caused downtimes and increasing failure dependencies resulted in build-ups of failures and caused significant degradations in network performance.

An Energy-Efficient Target-Tracking Strategy for Mobile Sensor Networks.

PubMed

Mahboubi, Hamid; Masoudimansour, Walid; Aghdam, Amir G; Sayrafian-Pour, Kamran

2017-02-01

In this paper, an energy-efficient strategy is proposed for tracking a moving target in an environment with obstacles, using a network of mobile sensors. Typically, the most dominant sources of energy consumption in a mobile sensor network are sensing, communication, and movement. The proposed algorithm first divides the field into a grid of sufficiently small cells. The grid is then represented by a graph whose edges are properly weighted to reflect the energy consumption of sensors. The proposed technique searches for near-optimal locations for the sensors in different time instants to route information from the target to destination, using a shortest path algorithm. Simulations confirm the efficacy of the proposed algorithm.

Neural networks for tracking of unknown SISO discrete-time nonlinear dynamic systems.

PubMed

Aftab, Muhammad Saleheen; Shafiq, Muhammad

2015-11-01

This article presents a Lyapunov function based neural network tracking (LNT) strategy for single-input, single-output (SISO) discrete-time nonlinear dynamic systems. The proposed LNT architecture is composed of two feedforward neural networks operating as controller and estimator. A Lyapunov function based back propagation learning algorithm is used for online adjustment of the controller and estimator parameters. The controller and estimator error convergence and closed-loop system stability analysis is performed by Lyapunov stability theory. Moreover, two simulation examples and one real-time experiment are investigated as case studies. The achieved results successfully validate the controller performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

The GPS Topex/Poseidon precise orbit determination experiment - Implications for design of GPS global networks

NASA Technical Reports Server (NTRS)

Lindqwister, Ulf J.; Lichten, Stephen M.; Davis, Edgar S.; Theiss, Harold L.

1993-01-01

Topex/Poseidon, a cooperative satellite mission between United States and France, aims to determine global ocean circulation patterns and to study their influence on world climate through precise measurements of sea surface height above the geoid with an on-board altimeter. To achieve the mission science aims, a goal of 13-cm orbit altitude accuracy was set. Topex/Poseidon includes a Global Positioning System (GPS) precise orbit determination (POD) system that has now demonstrated altitude accuracy better than 5 cm. The GPS POD system includes an on-board GPS receiver and a 6-station GPS global tracking network. This paper reviews early GPS results and discusses multi-mission capabilities available from a future enhanced global GPS network, which would provide ground-based geodetic and atmospheric calibrations needed for NASA deep space missions while also supplying tracking data for future low Earth orbiters. Benefits of the enhanced global GPS network include lower operations costs for deep space tracking and many scientific and societal benefits from the low Earth orbiter missions, including improved understanding of ocean circulation, ocean-weather interactions, the El Nino effect, the Earth thermal balance, and weather forecasting.

An Alternative Wearable Tracking System Based on a Low-Power Wide-Area Network

PubMed Central

Fernández-Garcia, Raul; Gil, Ignacio

2017-01-01

This work presents an alternative wearable tracking system based on a low-power wide area network. A complete GPS receiver was integrated with a textile substrate, and the latitude and longitude coordinates were sent to the cloud by means of the SIM-less SIGFOX network. To send the coordinates over SIGFOX protocol, a specific codification algorithm was used and a customized UHF antenna on jeans fabric was designed, simulated and tested. Moreover, to guarantee the compliance to international regulations for human body exposure to electromagnetic radiation, the electromagnetic specific absorption rate of this antenna was analyzed. A specific remote server was developed to decode the latitude and longitude coordinates. Once the coordinates have been decoded, the remote server sends this information to the open source data viewer SENTILO to show the location of the sensor node in a map. The functionality of this system has been demonstrated experimentally. The results guarantee the utility and wearability of the proposed tracking system for the development of sensor nodes and point out that it can be a low cost alternative to other commercial products based on GSM networks. PMID:28335424

Neural-Network-Based Robust Optimal Tracking Control for MIMO Discrete-Time Systems With Unknown Uncertainty Using Adaptive Critic Design.

PubMed

Liu, Lei; Wang, Zhanshan; Zhang, Huaguang

2018-04-01

This paper is concerned with the robust optimal tracking control strategy for a class of nonlinear multi-input multi-output discrete-time systems with unknown uncertainty via adaptive critic design (ACD) scheme. The main purpose is to establish an adaptive actor-critic control method, so that the cost function in the procedure of dealing with uncertainty is minimum and the closed-loop system is stable. Based on the neural network approximator, an action network is applied to generate the optimal control signal and a critic network is used to approximate the cost function, respectively. In contrast to the previous methods, the main features of this paper are: 1) the ACD scheme is integrated into the controllers to cope with the uncertainty and 2) a novel cost function, which is not in quadric form, is proposed so that the total cost in the design procedure is reduced. It is proved that the optimal control signals and the tracking errors are uniformly ultimately bounded even when the uncertainty exists. Finally, a numerical simulation is developed to show the effectiveness of the present approach.

Vision-based mobile robot navigation through deep convolutional neural networks and end-to-end learning

NASA Astrophysics Data System (ADS)

Zhang, Yachu; Zhao, Yuejin; Liu, Ming; Dong, Liquan; Kong, Lingqin; Liu, Lingling

2017-09-01

In contrast to humans, who use only visual information for navigation, many mobile robots use laser scanners and ultrasonic sensors along with vision cameras to navigate. This work proposes a vision-based robot control algorithm based on deep convolutional neural networks. We create a large 15-layer convolutional neural network learning system and achieve the advanced recognition performance. Our system is trained from end to end to map raw input images to direction in supervised mode. The images of data sets are collected in a wide variety of weather conditions and lighting conditions. Besides, the data sets are augmented by adding Gaussian noise and Salt-and-pepper noise to avoid overfitting. The algorithm is verified by two experiments, which are line tracking and obstacle avoidance. The line tracking experiment is proceeded in order to track the desired path which is composed of straight and curved lines. The goal of obstacle avoidance experiment is to avoid the obstacles indoor. Finally, we get 3.29% error rate on the training set and 5.1% error rate on the test set in the line tracking experiment, 1.8% error rate on the training set and less than 5% error rate on the test set in the obstacle avoidance experiment. During the actual test, the robot can follow the runway centerline outdoor and avoid the obstacle in the room accurately. The result confirms the effectiveness of the algorithm and our improvement in the network structure and train parameters

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

DOE PAGES

Farrell, Steven; Anderson, Dustin; Calafiura, Paolo; ...

2017-08-08

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problemmore » thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. Furthermore, we will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data.« less

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Farrell, Steven; Anderson, Dustin; Calafiura, Paolo

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problemmore » thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. Furthermore, we will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data.« less

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network.

PubMed

Han, Gaining; Fu, Weiping; Wang, Wen; Wu, Zongsheng

2017-05-30

The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control.

Probabilistic Multi-Person Tracking Using Dynamic Bayes Networks

NASA Astrophysics Data System (ADS)

Klinger, T.; Rottensteiner, F.; Heipke, C.

2015-08-01

Tracking-by-detection is a widely used practice in recent tracking systems. These usually rely on independent single frame detections that are handled as observations in a recursive estimation framework. If these observations are imprecise the generated trajectory is prone to be updated towards a wrong position. In contrary to existing methods our novel approach uses a Dynamic Bayes Network in which the state vector of a recursive Bayes filter, as well as the location of the tracked object in the image are modelled as unknowns. These unknowns are estimated in a probabilistic framework taking into account a dynamic model, and a state-of-the-art pedestrian detector and classifier. The classifier is based on the Random Forest-algorithm and is capable of being trained incrementally so that new training samples can be incorporated at runtime. This allows the classifier to adapt to the changing appearance of a target and to unlearn outdated features. The approach is evaluated on a publicly available benchmark. The results confirm that our approach is well suited for tracking pedestrians over long distances while at the same time achieving comparatively good geometric accuracy.

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network

PubMed Central

Han, Gaining; Fu, Weiping; Wang, Wen; Wu, Zongsheng

2017-01-01

The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control. PMID:28556817

Intelligent robust tracking control for a class of uncertain strict-feedback nonlinear systems.

PubMed

Chang, Yeong-Chan

2009-02-01

This paper addresses the problem of designing robust tracking controls for a large class of strict-feedback nonlinear systems involving plant uncertainties and external disturbances. The input and virtual input weighting matrices are perturbed by bounded time-varying uncertainties. An adaptive fuzzy-based (or neural-network-based) dynamic feedback tracking controller will be developed such that all the states and signals of the closed-loop system are bounded and the trajectory tracking error should be as small as possible. First, the adaptive approximators with linearly parameterized models are designed, and a partitioned procedure with respect to the developed adaptive approximators is proposed such that the implementation of the fuzzy (or neural network) basis functions depends only on the state variables but does not depend on the tuning approximation parameters. Furthermore, we extend to design the nonlinearly parameterized adaptive approximators. Consequently, the intelligent robust tracking control schemes developed in this paper possess the properties of computational simplicity and easy implementation. Finally, simulation examples are presented to demonstrate the effectiveness of the proposed control algorithms.

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

NASA Astrophysics Data System (ADS)

Farrell, Steven; Anderson, Dustin; Calafiura, Paolo; Cerati, Giuseppe; Gray, Lindsey; Kowalkowski, Jim; Mudigonda, Mayur; Prabhat; Spentzouris, Panagiotis; Spiropoulou, Maria; Tsaris, Aristeidis; Vlimant, Jean-Roch; Zheng, Stephan

2017-08-01

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. We will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data.

Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

USGS Publications Warehouse

Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

1997-01-01

One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

Describing environmental public health data: implementing a descriptive metadata standard on the environmental public health tracking network.

PubMed

Patridge, Jeff; Namulanda, Gonza

2008-01-01

The Environmental Public Health Tracking (EPHT) Network provides an opportunity to bring together diverse environmental and health effects data by integrating}?> local, state, and national databases of environmental hazards, environmental exposures, and health effects. To help users locate data on the EPHT Network, the network will utilize descriptive metadata that provide critical information as to the purpose, location, content, and source of these data. Since 2003, the Centers for Disease Control and Prevention's EPHT Metadata Subgroup has been working to initiate the creation and use of descriptive metadata. Efforts undertaken by the group include the adoption of a metadata standard, creation of an EPHT-specific metadata profile, development of an open-source metadata creation tool, and promotion of the creation of descriptive metadata by changing the perception of metadata in the public health culture.

Shifting Pacific storm tracks as stressors to ecosystems of western North America.

PubMed

Dannenberg, Matthew P; Wise, Erika K

2017-11-01

Much of the precipitation delivered to western North America arrives during the cool season via midlatitude Pacific storm tracks, which may experience future shifts in response to climate change. Here, we assess the sensitivity of the hydroclimate and ecosystems of western North America to the latitudinal position of cool-season Pacific storm tracks. We calculated correlations between storm track variability and three hydroclimatic variables: gridded cool-season standardized precipitation-evapotranspiration index, April snow water equivalent, and water year streamflow from a network of USGS stream gauges. To assess how historical storm track variability affected ecosystem processes, we derived forest growth estimates from a large network of tree-ring widths and land surface phenology and wildfire estimates from remote sensing. From 1980 to 2014, cool-season storm tracks entered western North America between approximately 41°N and 53°N. Cool-season moisture supply and snowpack responded strongly to storm track position, with positive correlations to storm track latitude in eastern Alaska and northwestern Canada but negative correlations in the northwestern U.S. Ecosystems of the western United States were greener and more productive following winters with south-shifted storm tracks, while Canadian ecosystems were greener in years when the cool-season storm track was shifted to the north. On average, larger areas of the northwestern United States were burned by moderate to high severity wildfires when storm tracks were displaced north, and the average burn area per fire also tended to be higher in years with north-shifted storm tracks. These results suggest that projected shifts of Pacific storm tracks over the 21st century would likely alter hydroclimatic and ecological regimes in western North America, particularly in the northwestern United States, where moisture supply and ecosystem processes are highly sensitive to the position of cool-season storm tracks. © 2017 John Wiley & Sons Ltd.

GRAVSAT/GEOPAUSE refraction study

NASA Technical Reports Server (NTRS)

Llewellyn, S. K.

1977-01-01

A ground station network tracked a high altitude spacecraft which in turn tracked a low orbiting satellite. Orbit data are relayed back to the ground stations. A refraction study was performed on this configuration to compute ionospheric and tropospheric refraction effects along the satellite and ground links.

Boarding Team Networking on the Move: Applying Unattended Relay Nodes

DTIC Science & Technology

2014-09-01

below the main deck via a wireless ad-hoc network will enhance the situational awareness. Regarding the boarding of a non-compliant vessel, tracking...reaction time. 14. SUBJECT TERMS Maritime Interdiction Operations, Boarding Team Networking , Unattended Relay Nodes, Wireless Mesh Networks Onboard...the steel structures of naval vessels obstruct signals to propagate below the main deck. Extending the network below the main deck via a wireless ad

Evaluating the Network: A Workflow for Tracking Twitter Interactions Using Social Networking Analysis

ERIC Educational Resources Information Center

Goodier, Sarah

2018-01-01

Networking plays an important role in research projects to build a community and audience around a research area. Using social media is popular in project communication as it provides the ability to engage with a group of followers daily. Such online networking tools provide the advantage of providing nearrealtime data, which can be used to…

Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems †

PubMed Central

Kim, Kwangsoo; Jin, Jae-Yeon; Jin, Seong-il

2016-01-01

Medical asset tracking systems track a medical device with a mobile node and determine its status as either in or out, because it can leave a monitoring area. Due to a failed node, this system may decide that a mobile asset is outside the area, even though it is within the area. In this paper, an efficient classification method is proposed to separate mobile nodes disconnected from a wireless sensor network between nodes with faults and a node that actually has left the monitoring region. The proposed scheme uses two trends extracted from the neighboring nodes of a disconnected mobile node. First is the trend in a series of the neighbor counts; the second is that of the ratios of the boundary nodes included in the neighbors. Based on such trends, the proposed method separates failed nodes from mobile nodes that are disconnected from a wireless sensor network without failures. The proposed method is evaluated using both real data generated from a medical asset tracking system and also using simulations with the network simulator (ns-2). The experimental results show that the proposed method correctly differentiates between failed nodes and nodes that are no longer in the monitoring region, including the cases that the conventional methods fail to detect. PMID:26901200

Intelligent complementary sliding-mode control for LUSMS-based X-Y-theta motion control stage.

PubMed

Lin, Faa-Jeng; Chen, Syuan-Yi; Shyu, Kuo-Kai; Liu, Yen-Hung

2010-07-01

An intelligent complementary sliding-mode control (ICSMC) system using a recurrent wavelet-based Elman neural network (RWENN) estimator is proposed in this study to control the mover position of a linear ultrasonic motors (LUSMs)-based X-Y-theta motion control stage for the tracking of various contours. By the addition of a complementary generalized error transformation, the complementary sliding-mode control (CSMC) can efficiently reduce the guaranteed ultimate bound of the tracking error by half compared with the slidingmode control (SMC) while using the saturation function. To estimate a lumped uncertainty on-line and replace the hitting control of the CSMC directly, the RWENN estimator is adopted in the proposed ICSMC system. In the RWENN, each hidden neuron employs a different wavelet function as an activation function to improve both the convergent precision and the convergent time compared with the conventional Elman neural network (ENN). The estimation laws of the RWENN are derived using the Lyapunov stability theorem to train the network parameters on-line. A robust compensator is also proposed to confront the uncertainties including approximation error, optimal parameter vectors, and higher-order terms in Taylor series. Finally, some experimental results of various contours tracking show that the tracking performance of the ICSMC system is significantly improved compared with the SMC and CSMC systems.

Positioning and tracking control system analysis for mobile free space optical network

NASA Astrophysics Data System (ADS)

Li, Yushan; Refai, Hazem; Sluss, , James J., Jr.; Verma, Pramode; LoPresti, Peter

2005-08-01

Free Space Optical (FSO) communication has evolved to be applied to the mobile network, because it can provide up to 2.5Gbps or higher data rate wireless communication. One of the key challenges with FSO systems is to maintain the Line of Sight (LOS) between transmitter and receiver. In this paper, the feasibility and performance of applying the FSO technology to the mobile network is explored, and the design plan of the attitude positioning and tracking control system of the FSO transceiver is investigated. First, the system architecture is introduced, the requirements for the control system are analyzed, the involved reference frames and frame transformation are presented. Second, the control system bandwidth is used to evaluate the system performance in controlling a positioning system consisting of a gimbal and a steering mirror, some definitions to describe the positioning accuracy and tracking capacity are given. The attitude control of a FSO transceiver is split into 2 similar channels: pitch and yaw. Using an equivalent linear control system model, the simulations are carried out, with and without the presence of uncertainties that includes GPS data errors and sensor measurement errors. Finally, based on the simulation results in the pitch channel, the quantitative evaluation on the performance of the control system is given, including positioning accuracy, tracking capability and uncertainty tolerance.

Vital signs monitoring and patient tracking over a wireless network.

PubMed

Gao, Tia; Greenspan, Dan; Welsh, Matt; Juang, Radford; Alm, Alex

2005-01-01

Patients at a disaster scene can greatly benefit from technologies that continuously monitor their vital status and track their locations until they are admitted to the hospital. We have designed and developed a real-time patient monitoring system that integrates vital signs sensors, location sensors, ad-hoc networking, electronic patient records, and web portal technology to allow remote monitoring of patient status. This system shall facilitate communication between providers at the disaster scene, medical professionals at local hospitals, and specialists available for consultation from distant facilities.

SSC Tenant Meeting: NASA Near Earth Network (NEN) Overview

NASA Technical Reports Server (NTRS)

Carter, David; Larsen, David; Baldwin, Philip; Wilson, Cristy; Ruley, LaMont

2018-01-01

The Near Earth Network (NEN) consists of globally distributed tracking stations that are strategically located throughout the world which provide Telemetry, Tracking, and Commanding (TTC) services support to a variety of orbital and suborbital flight missions, including Low Earth Orbit (LEO), Geosynchronous Earth Orbit (GEO), highly elliptical, and lunar orbits. Swedish Space Corporation (SSC), which is one of the NEN Commercial Service Provider, has provided the NEN with TTC services support from its Alaska, Hawaii, Chile and Sweden. The presentation will give an overview of the NEN and its support from SSC.

Tracking and data system support for the Pioneer project. Volume 2: Pioneer 11 prelaunch planning through second trajectory correction, to 1 May 1973

NASA Technical Reports Server (NTRS)

Barton, W. R.; Miller, R. B.

1975-01-01

The tracking and data system support of the planning, testing, launch, near-earth, and deep space phases of the Pioneer 11 Jupiter Mission are described, including critical phases of spacecraft flight and guidance. Scientific instruments aboard the spacecraft registered information relative to interplanetary particles and fields. Knowledge of the celestial mechanics of the solar system was improved through radiometric data gathering. Network performance, details of network support activity, and special support activities are discussed.

A z-Vertex Trigger for Belle II

NASA Astrophysics Data System (ADS)

Skambraks, S.; Abudinén, F.; Chen, Y.; Feindt, M.; Frühwirth, R.; Heck, M.; Kiesling, C.; Knoll, A.; Neuhaus, S.; Paul, S.; Schieck, J.

2015-08-01

The Belle II experiment will go into operation at the upgraded SuperKEKB collider in 2016. SuperKEKB is designed to deliver an instantaneous luminosity L = 8 ×1035 cm - 2 s - 1. The experiment will therefore have to cope with a much larger machine background than its predecessor Belle, in particular from events outside of the interaction region. We present the concept of a track trigger, based on a neural network approach, that is able to suppress a large fraction of this background by reconstructing the z (longitudinal) position of the event vertex within the latency of the first level trigger. The trigger uses the hit information from the Central Drift Chamber (CDC) of Belle II within narrow cones in polar and azimuthal angle as well as in transverse momentum (“sectors”), and estimates the z-vertex without explicit track reconstruction. The preprocessing for the track trigger is based on the track information provided by the standard CDC trigger. It takes input from the 2D track finder, adds information from the stereo wires of the CDC, and finds the appropriate sectors in the CDC for each track. Within the sector, the z-vertex is estimated by a specialized neural network, with the drift times from the CDC as input and a continuous output corresponding to the scaled z-vertex. The neural algorithm will be implemented in programmable hardware. To this end a Virtex 7 FPGA board will be used, which provides at present the most promising solution for a fully parallelized implementation of neural networks or alternative multivariate methods. A high speed interface for external memory will be integrated into the platform, to be able to store the O(109) parameters required. The contribution presents the results of our feasibility studies and discusses the details of the envisaged hardware solution.

Tracking Data Certification for the Lunar Reconnaissance Orbiter

NASA Technical Reports Server (NTRS)

Morinelli, Patrick J.; Socoby, Joseph; Hendry, Steve; Campion, Richard

2010-01-01

This paper details the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) Flight Dynamics Facility (FDF) tracking data certification effort of the Lunar Reconnaissance Orbiter (LRO) Space Communications Network (SCN) complement of tracking stations consisting of the NASA White Sands 1 antenna (WS1), and the commercial provider Universal Space Network (USN) antennas at South Point, Hawaii; Dongara Australia; Weilheim, Germany; and Kiruna, Sweden. Certification assessment required the cooperation and coordination of parties not under the control of either the LRO project or ground stations as uplinks on cooperating spacecraft were necessary. The LRO range-tracking requirement of 10m 1 sigma could be satisfactorily demonstrated using any typical spacecraft capable of range tracking. Though typical Low Earth Orbiting (LEO) or Geosynchronous Earth Orbiting (GEO) spacecraft may be adequate for range certification, their measurement dynamics and noise would be unacceptable for proper Doppler certification of 1-3mm/sec 1 sigma. As LRO will orbit the Moon, it was imperative that a suitable target spacecraft be utilized which can closely mimic the expected lunar orbital Doppler dynamics of +/-1.6km/sec and +/-1.5m/sq sec to +/-0.15m/sq sec, is in view of the ground stations, supports coherent S-Band Doppler tracking measurements, and can be modeled by the FDF. In order to meet the LRO metric tracking data specifications, the SCN ground stations employed previously uncertified numerically controlled tracking receivers. Initial certification testing revealed certain characteristics of the units that required resolution before being granted certification.

GRC-2010-C-05148

NASA Image and Video Library

2006-11-08

Communications, Navigation, and Network Reconfigurable Test-bed (CoNNeCT) Flight Hardware Compatibility Test Sets - Glenn Research Center and Networks Integration Management Office (NIMO) Testing for the Tracking and Data Relay Satellite System (TDRSS) - Goddard Space Flight Center Testing

GRC-2010-C-05136

NASA Image and Video Library

2006-11-16

Communications, Navigation, and Network Reconfigurable Test-bed (CoNNeCT) Flight Hardware Compatibility Test Sets - Glenn Research Center and Networks Integration Management Office (NIMO) Testing for the Tracking and Data Relay Satellite System (TDRSS) - Goddard Space Flight Center Testing

Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search.

PubMed

Liu, Meiqin; Zhang, Duo; Zhang, Senlin; Zhang, Qunfei

2017-12-04

Underwater wireless sensor networks (UWSNs) can provide a promising solution to underwater target tracking. Due to the limited computation and bandwidth resources, only a small part of nodes are selected to track the target at each interval. How to improve tracking accuracy with a small number of nodes is a key problem. In recent years, a node depth adjustment system has been developed and applied to issues of network deployment and routing protocol. As far as we know, all existing tracking schemes keep underwater nodes static or moving with water flow, and node depth adjustment has not been utilized for underwater target tracking yet. This paper studies node depth adjustment method for target tracking in UWSNs. Firstly, since a Fisher Information Matrix (FIM) can quantify the estimation accuracy, its relation to node depth is derived as a metric. Secondly, we formulate the node depth adjustment as an optimization problem to determine moving depth of activated node, under the constraint of moving range, the value of FIM is used as objective function, which is aimed to be minimized over moving distance of nodes. Thirdly, to efficiently solve the optimization problem, an improved Harmony Search (HS) algorithm is proposed, in which the generating probability is modified to improve searching speed and accuracy. Finally, simulation results are presented to verify performance of our scheme.

Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search

PubMed Central

Zhang, Senlin; Zhang, Qunfei

2017-01-01

Underwater wireless sensor networks (UWSNs) can provide a promising solution to underwater target tracking. Due to the limited computation and bandwidth resources, only a small part of nodes are selected to track the target at each interval. How to improve tracking accuracy with a small number of nodes is a key problem. In recent years, a node depth adjustment system has been developed and applied to issues of network deployment and routing protocol. As far as we know, all existing tracking schemes keep underwater nodes static or moving with water flow, and node depth adjustment has not been utilized for underwater target tracking yet. This paper studies node depth adjustment method for target tracking in UWSNs. Firstly, since a Fisher Information Matrix (FIM) can quantify the estimation accuracy, its relation to node depth is derived as a metric. Secondly, we formulate the node depth adjustment as an optimization problem to determine moving depth of activated node, under the constraint of moving range, the value of FIM is used as objective function, which is aimed to be minimized over moving distance of nodes. Thirdly, to efficiently solve the optimization problem, an improved Harmony Search (HS) algorithm is proposed, in which the generating probability is modified to improve searching speed and accuracy. Finally, simulation results are presented to verify performance of our scheme. PMID:29207541

Automated tracking for advanced satellite laser ranging systems

NASA Astrophysics Data System (ADS)

McGarry, Jan F.; Degnan, John J.; Titterton, Paul J., Sr.; Sweeney, Harold E.; Conklin, Brion P.; Dunn, Peter J.

1996-06-01

NASA's Satellite Laser Ranging Network was originally developed during the 1970's to track satellites carrying corner cube reflectors. Today eight NASA systems, achieving millimeter ranging precision, are part of a global network of more than 40 stations that track 17 international satellites. To meet the tracking demands of a steadily growing satellite constellation within existing resources, NASA is embarking on a major automation program. While manpower on the current systems will be reduced to a single operator, the fully automated SLR2000 system is being designed to operate for months without human intervention. Because SLR2000 must be eyesafe and operate in daylight, tracking is often performed in a low probability of detection and high noise environment. The goal is to automatically select the satellite, setup the tracking and ranging hardware, verify acquisition, and close the tracking loop to optimize data yield. TO accomplish the autotracking tasks, we are investigating (1) improved satellite force models, (2) more frequent updates of orbital ephemerides, (3) lunar laser ranging data processing techniques to distinguish satellite returns from noise, and (4) angular detection and search techniques to acquire the satellite. A Monte Carlo simulator has been developed to allow optimization of the autotracking algorithms by modeling the relevant system errors and then checking performance against system truth. A combination of simulator and preliminary field results will be presented.

Neural-Network-Based Adaptive Decentralized Fault-Tolerant Control for a Class of Interconnected Nonlinear Systems.

PubMed

Li, Xiao-Jian; Yang, Guang-Hong

2018-01-01

This paper is concerned with the adaptive decentralized fault-tolerant tracking control problem for a class of uncertain interconnected nonlinear systems with unknown strong interconnections. An algebraic graph theory result is introduced to address the considered interconnections. In addition, to achieve the desirable tracking performance, a neural-network-based robust adaptive decentralized fault-tolerant control (FTC) scheme is given to compensate the actuator faults and system uncertainties. Furthermore, via the Lyapunov analysis method, it is proven that all the signals of the resulting closed-loop system are semiglobally bounded, and the tracking errors of each subsystem exponentially converge to a compact set, whose radius is adjustable by choosing different controller design parameters. Finally, the effectiveness and advantages of the proposed FTC approach are illustrated with two simulated examples.

Architectural Design for European SST System

NASA Astrophysics Data System (ADS)

Utzmann, Jens; Wagner, Axel; Blanchet, Guillaume; Assemat, Francois; Vial, Sophie; Dehecq, Bernard; Fernandez Sanchez, Jaime; Garcia Espinosa, Jose Ramon; Agueda Mate, Alberto; Bartsch, Guido; Schildknecht, Thomas; Lindman, Niklas; Fletcher, Emmet; Martin, Luis; Moulin, Serge

2013-08-01

The paper presents the results of a detailed design, evaluation and trade-off of a potential European Space Surveillance and Tracking (SST) system architecture. The results have been produced in study phase 1 of the on-going "CO-II SSA Architectural Design" project performed by the Astrium consortium as part of ESA's Space Situational Awareness Programme and are the baseline for further detailing and consolidation in study phase 2. The sensor network is comprised of both ground- and space-based assets and aims at being fully compliant with the ESA SST System Requirements. The proposed ground sensors include a surveillance radar, an optical surveillance system and a tracking network (radar and optical). A space-based telescope system provides significant performance and robustness for the surveillance and tracking of beyond-LEO target objects.

Study of tracking and data acquisition system for the 1990's. Volume 4: TDAS space segment architecture

NASA Technical Reports Server (NTRS)

Orr, R. S.

1984-01-01

Tracking and data acquisition system (TDAS) requirements, TDAS architectural goals, enhanced TDAS subsystems, constellation and networking options, TDAS spacecraft options, crosslink implementation, baseline TDAS space segment architecture, and treat model development/security analysis are addressed.

A game theory approach to target tracking in sensor networks.

PubMed

Gu, Dongbing

2011-02-01

In this paper, we investigate a moving-target tracking problem with sensor networks. Each sensor node has a sensor to observe the target and a processor to estimate the target position. It also has wireless communication capability but with limited range and can only communicate with neighbors. The moving target is assumed to be an intelligent agent, which is "smart" enough to escape from the detection by maximizing the estimation error. This adversary behavior makes the target tracking problem more difficult. We formulate this target estimation problem as a zero-sum game in this paper and use a minimax filter to estimate the target position. The minimax filter is a robust filter that minimizes the estimation error by considering the worst case noise. Furthermore, we develop a distributed version of the minimax filter for multiple sensor nodes. The distributed computation is implemented via modeling the information received from neighbors as measurements in the minimax filter. The simulation results show that the target tracking algorithm proposed in this paper provides a satisfactory result.

Tracking and data system support for the Pioneer project. Volume 1: Pioneer 10-prelaunch planning through second trajectory correction, 4 December 1969 - 1 April 1972

NASA Technical Reports Server (NTRS)

Siegmeth, A. J.; Purdue, R. E.; Ryan, R. E.

1973-01-01

The tracking and data system support of the launch, near-earth, and deep space phases of the Pioneer 10 mission, which sent a Pioneer spacecraft into a flyby of Jupiter that would eventually allow the spacecraft to escape the solar system is discussed. The support through the spacecraft's second trajectory correction is reported. During this period, scientific instruments aboard the spacecraft registered information relative to interplanetary particles and fields, and radiometric data generated by the network continued to improve knowledge of the celestial mechanics of the solar system. In addition to network support activity detail, network performance and special support activities are covered.

Engineering evaluations and studies. Report for IUS studies

NASA Technical Reports Server (NTRS)

1981-01-01

The reviews, investigations, and analyses of the Inertial Upper Stage (IUS) Spacecraft Tracking and Data Network (STDN) transponder are reviewed. Carrier lock detector performance for Tracking and Data Relay Satellite System (TDRSS) dual-mode operation is discussed, as is the problem of predicting instantaneous frequency error in the carrier loop. Coastal loop performance analysis is critiqued and the static tracking phase error induced by thermal noise biases is discussed.

Supply support of NASA tracking networks

NASA Technical Reports Server (NTRS)

1973-01-01

The extent which supply support for Jet Propulsion Laboratory's Deep Space Network and Goddard Space Flight Center's Space Flight Tracking and Data Network should be consolidated is considered along with the Identification of opportunities for improvements in each of the supply systems without regard to consolidation. There is a considerable amount of commonality between the items in the stock catalogs at the two network depots, 58% for federal stock number items and 30% overall. The workload at the DSIF Supply Depot (DSD) is small (less than 20%) compared to the Network Logistics Depot (NLD). A number of important benefits in supply support would result from a consolidation of DSD into NLD. LMI found that a consolidation as is, without any changes in inventory management techniques, would reduce annual operating costs by from $208,000 to $358,000. However, if the consolidation were coupled with a change to use of economic order quantities, the annual operating cost reduction would range from $930,000 to $1,078,000.

Receivers

NASA Technical Reports Server (NTRS)

Donnelly, H.

1983-01-01

Before discussing Deep Space Network receivers, a brief description of the functions of receivers and how they interface with other elements of the Network is presented. Different types of receivers are used in the Network for various purposes. The principal receiver type is used for telemetry and tracking. This receiver provides the capability, with other elements of the Network, to track the space probe utilizing Doppler and range measurements, and to receive telemetry, including both scientific data from the onboard experiments and engineering data pertaining to the health of the probe. Another type of receiver is used for radio science applications. This receiver measures phase perturbations on the carrier signal to obtain information on the composition of solar and planetary atmospheres and interplanetary space. A third type of receiver utilizes very long baseline interferometry (VLBI) techniques for both radio science and spacecraft navigation data. Only the telemetry receiver is described in detail in this document. The integration of the Receiver-Exciter subsystem with other portions of the Deep Space Network is described.

A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multirate Networked Industrial Process Control.

PubMed

Wang, Tong; Gao, Huijun; Qiu, Jianbin

2016-02-01

This paper investigates the multirate networked industrial process control problem in double-layer architecture. First, the output tracking problem for sampled-data nonlinear plant at device layer with sampling period T(d) is investigated using adaptive neural network (NN) control, and it is shown that the outputs of subsystems at device layer can track the decomposed setpoints. Then, the outputs and inputs of the device layer subsystems are sampled with sampling period T(u) at operation layer to form the index prediction, which is used to predict the overall performance index at lower frequency. Radial basis function NN is utilized as the prediction function due to its approximation ability. Then, considering the dynamics of the overall closed-loop system, nonlinear model predictive control method is proposed to guarantee the system stability and compensate the network-induced delays and packet dropouts. Finally, a continuous stirred tank reactor system is given in the simulation part to demonstrate the effectiveness of the proposed method.

Numerical Experiments on Advective Transport in Large Three-Dimensional Discrete Fracture Networks

NASA Astrophysics Data System (ADS)

Makedonska, N.; Painter, S. L.; Karra, S.; Gable, C. W.

2013-12-01

Modeling of flow and solute transport in discrete fracture networks is an important approach for understanding the migration of contaminants in impermeable hard rocks such as granite, where fractures provide dominant flow and transport pathways. The discrete fracture network (DFN) model attempts to mimic discrete pathways for fluid flow through a fractured low-permeable rock mass, and may be combined with particle tracking simulations to address solute transport. However, experience has shown that it is challenging to obtain accurate transport results in three-dimensional DFNs because of the high computational burden and difficulty in constructing a high-quality unstructured computational mesh on simulated fractures. An integrated DFN meshing [1], flow, and particle tracking [2] simulation capability that enables accurate flow and particle tracking simulation on large DFNs has recently been developed. The new capability has been used in numerical experiments on advective transport in large DFNs with tens of thousands of fractures and millions of computational cells. The modeling procedure starts from the fracture network generation using a stochastic model derived from site data. A high-quality computational mesh is then generated [1]. Flow is then solved using the highly parallel PFLOTRAN [3] code. PFLOTRAN uses the finite volume approach, which is locally mass conserving and thus eliminates mass balance problems during particle tracking. The flow solver provides the scalar fluxes on each control volume face. From the obtained fluxes the Darcy velocity is reconstructed for each node in the network [4]. Velocities can then be continuously interpolated to any point in the domain of interest, thus enabling random walk particle tracking. In order to describe the flow field on fractures intersections, the control volume cells on intersections are split into four planar polygons, where each polygon corresponds to a piece of a fracture near the intersection line. Thus, computational nodes lying on fracture intersections have four associated velocities, one on each side of the intersection in each fracture plane [2]. This information is used to route particles arriving at the fracture intersection to the appropriate downstream fracture segment. Verified for small DFNs, the new simulation capability allows accurate particle tracking on more realistic representations of fractured rock sites. In the current work we focus on travel time statistics and spatial dispersion and show numerical results in DFNs of different sizes, fracture densities, and transmissivity distributions. [1] Hyman J.D., Gable C.W., Painter S.L., Automated meshing of stochastically generated discrete fracture networks, Abstract H33G-1403, 2011 AGU, San Francisco, CA, 5-9 Dec. [2] N. Makedonska, S. L. Painter, T.-L. Hsieh, Q.M. Bui, and C. W. Gable., Development and verification of a new particle tracking capability for modeling radionuclide transport in discrete fracture networks, Abstract, 2013 IHLRWM, Albuquerque, NM, Apr. 28 - May 3. [3] Lichtner, P.C., Hammond, G.E., Bisht, G., Karra, S., Mills, R.T., and Kumar, J. (2013) PFLOTRAN User's Manual: A Massively Parallel Reactive Flow Code. [4] Painter S.L., Gable C.W., Kelkar S., Pathline tracing on fully unstructured control-volume grids, Computational Geosciences, 16 (4), 2012, 1125-1134.

Students' Informal Peer Feedback Networks

ERIC Educational Resources Information Center

Headington, Rita

2018-01-01

The nature and significance of students' informal peer feedback networks is an under-explored area. This paper offers the findings of a longitudinal investigation of the informal peer feedback networks of a cohort of student teachers [n = 105] across the three years of a UK primary education degree programme. It tracked the dynamic nature of these…

The deep space network, volume 12

NASA Technical Reports Server (NTRS)

1972-01-01

Progress in the development of the DSN is reported along with TDA research and technology, network engineering, hardware, and software implementation. Included are descriptions of the DSN function and facilities, Helios mission support, Mariner Venus/Mercury 1973 mission support, Viking mission support, tracking and ground-based navigation, communications, network control and data processing, and deep space stations.

Marine Vehicle Sensor Network Architecture and Protocol Designs for Ocean Observation

PubMed Central

Zhang, Shaowei; Yu, Jiancheng; Zhang, Aiqun; Yang, Lei; Shu, Yeqiang

2012-01-01

The micro-scale and meso-scale ocean dynamic processes which are nonlinear and have large variability, have a significant impact on the fisheries, natural resources, and marine climatology. A rapid, refined and sophisticated observation system is therefore needed in marine scientific research. The maneuverability and controllability of mobile sensor platforms make them a preferred choice to establish ocean observing networks, compared to the static sensor observing platform. In this study, marine vehicles are utilized as the nodes of mobile sensor networks for coverage sampling of a regional ocean area and ocean feature tracking. A synoptic analysis about marine vehicle dynamic control, multi vehicles mission assignment and path planning methods, and ocean feature tracking and observing techniques is given. Combined with the observation plan in the South China Sea, we provide an overview of the mobile sensor networks established with marine vehicles, and the corresponding simulation results. PMID:22368475

Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

DOE PAGES

Liu, Qiang; Brigham, Katharine; Rao, Nageswara S. V.

2017-02-01

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics – such as the position – is expected to possess much improved quality. In this paper, we pursue learning-based approaches for estimation and fusion of target states in longhaul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). Finally, the joint effect of (i)more » imperfect communication condition, namely, link-level loss and delay, and (ii) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies.« less

Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Qiang; Brigham, Katharine; Rao, Nageswara S. V.

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics – such as the position – is expected to possess much improved quality. In this paper, we pursue learning-based approaches for estimation and fusion of target states in longhaul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). Finally, the joint effect of (i)more » imperfect communication condition, namely, link-level loss and delay, and (ii) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies.« less

Radar coordination and resource management in a distributed sensor network using emergent control

NASA Astrophysics Data System (ADS)

Weir, B. S.; Sokol, T. M.

2009-05-01

As the list of anti-air warfare and ballistic missile defense missions grows, there is an increasing need to coordinate and optimize usage of radar resources across the netted force. Early attempts at this optimization involved top-down control mechanisms whereby sensors accept resource tasking orders from networked tracking elements. These approaches rely heavily on uncertain knowledge of sensor constraints and capabilities. Furthermore, advanced sensor systems may support self-defense missions of the host platform and are therefore unable to relinquish control to an external function. To surmount these issues, the use of bottom-up emergent control techniques is proposed. The information necessary to make quality, network-wide resource allocations is readily available to sensor nodes with access to a netted track picture. By assessing resource priorities relative to the network (versus local) track picture, sensors can understand the contribution of their resources to the netted force. This allows the sensors to apply resources where most needed and remove waste. Furthermore, simple local rules for resource usage, when properly constructed, allow sensors to obtain a globally optimal resource allocation without direct coordination (emergence). These results are robust to partial implementation (i.e., not all nodes upgraded at once) and failures on individual nodes (whether from casualty or reallocation to other sensor missions), and they leave resource control decisions in the hands of the sensor systems instead of an external function. This paper presents independent research and development work on emergent control of sensor resources and the impact to resource allocation and tracking performance.

Design and simulation of sensor networks for tracking Wifi users in outdoor urban environments

NASA Astrophysics Data System (ADS)

Thron, Christopher; Tran, Khoi; Smith, Douglas; Benincasa, Daniel

2017-05-01

We present a proof-of-concept investigation into the use of sensor networks for tracking of WiFi users in outdoor urban environments. Sensors are fixed, and are capable of measuring signal power from users' WiFi devices. We derive a maximum likelihood estimate for user location based on instantaneous sensor power measurements. The algorithm takes into account the effects of power control, and is self-calibrating in that the signal power model used by the location algorithm is adjusted and improved as part of the operation of the network. Simulation results to verify the system's performance are presented. The simulation scenario is based on a 1.5 km2 area of lower Manhattan, The self-calibration mechanism was verified for initial rms (root mean square) errors of up to 12 dB in the channel power estimates: rms errors were reduced by over 60% in 300 track-hours, in systems with limited power control. Under typical operating conditions with (without) power control, location rms errors are about 8.5 (5) meters with 90% accuracy within 9 (13) meters, for both pedestrian and vehicular users. The distance error distributions for smaller distances (<30 m) are well-approximated by an exponential distribution, while the distributions for large distance errors have fat tails. The issue of optimal sensor placement in the sensor network is also addressed. We specify a linear programming algorithm for determining sensor placement for networks with reduced number of sensors. In our test case, the algorithm produces a network with 18.5% fewer sensors with comparable accuracy estimation performance. Finally, we discuss future research directions for improving the accuracy and capabilities of sensor network systems in urban environments.

The deep space network, volume 13

NASA Technical Reports Server (NTRS)

1973-01-01

The objectives, functions, and organization of the Deep Space Network are summarized. The deep space instrumentation facility, the ground communications facility, and the network control system are described. Other areas reported include: Helios Mission support, DSN support of the Mariner Mars 1971 extended mission, Mariner Venus/Mercury 1973 mission support, Viking mission support, radio science, tracking and ground-based navigation, network control and data processing, and deep space stations.

Orbit and clock determination of BDS regional navigation satellite system based on IGS M-GEX and WHU BETS tracking network

NASA Astrophysics Data System (ADS)

GENG, T.; Zhao, Q.; Shi, C.; Shum, C.; Guo, J.; Su, X.

2013-12-01

BeiDou Navigation Satellite System (BDS) began to provide the regional open service on December 27th 2012 and will provide the global open service by the end of 2020. Compared to GPS, the space segment of BDS Regional System consists of 5 Geostationary Earth Orbit satellites (GEO), 5 Inclined Geosynchronous Orbit satellites (IGSO) and 4 Medium Earth orbit (MEO) satellites. Since 2011, IGS Multiple-GNSS Experiment (M-GEX) focuses on tracking the newly available GNSS signals. This includes all signals from the modernized satellites of the GPS and GLONASS systems, as well as signals of the BDS, Galileo and QZSS systems. Up to now, BDS satellites are tracked by around 25 stations with a variety of different antennas and receivers from different GNSS manufacture communities in M-GEX network. Meanwhile, there are 17 stations with Unicore Communications Incorporation's GPS/BDS receivers in BeiDou Experimental Tracking Stations (BETS) network by Wuhan University. In addition, 5 BDS satellites have been tracking by the International Laser Ranging Service (ILRS). BDS performance is expected to be further studied by the GNSS communities. Following an introduction of the BDS system and above different tracking network, this paper discusses the achieved BDS characterization and performance assessment. Firstly, the BDS signal and measurement quality are analyzed with different antennas and receivers in detail compared to GPS. This includes depth of coverage for satellite observation, carrier-to-noise-density ratios, code noise and multipath, carrier phase errors. Secondly, BDS Precise Orbit Determination (POD) is processed. Different arc lengths and sets of orbit parameters are tested using Position And Navigation Data Analysis software (PANDA) which is developed at the Wuhan University. GEO, IGSO and MEO satellites orbit quality will be assessed using overlap comparison, 2-day orbit fit and external validations with Satellite Laser Range (SLR). Then BDS satellites are equipped with Rubidium clocks and clocks performance are also presented. Finally, benefits of BDS processing strategies and further developments are concluded.

System and method for tracking a signal source. [employing feedback control

NASA Technical Reports Server (NTRS)

Mogavero, L. N.; Johnson, E. G.; Evans, J. M., Jr.; Albus, J. S. (Inventor)

1978-01-01

A system for tracking moving signal sources is disclosed which is particularly adaptable for use in tracking stage performers. A miniature transmitter is attached to the person or object to be tracked and emits a detectable signal of a predetermined frequency. A plurality of detectors positioned in a preset pattern sense the signal and supply output information to a phase detector which applies signals representing the angular orientation of the transmitter to a computer. The computer provides command signals to a servo network which drives a device such as a motor driven mirror reflecting the beam of a spotlight, to track the moving transmitter.

Structure-function relationship of biological gels revealed by multiple-particle tracking and differential interference contrast microscopy: The case of human lamin networks

NASA Astrophysics Data System (ADS)

Panorchan, Porntula; Wirtz, Denis; Tseng, Yiider

2004-10-01

Lamin B1 filaments organize into a thin dense meshwork underlying the nucleoplasmic side of the nuclear envelope. Recent experiments in vivo suggest that lamin B1 plays a key structural role in the nuclear envelope, but the intrinsic mechanical properties of lamin B1 networks remain unknown. To assess the potential mechanical contribution of lamin B1 in maintaining the integrity and providing structural support to the nucleus, we measured the micromechanical properties and examined the ultrastructural distribution of lamin B1 networks in vitro using particle tracking methods and differential interference contrast (DIC) microscopy. We exploit various surface chemistries of the probe microspheres (carboxylated, polyethylene glycol-coated, and amine-modified) to differentiate lamin-rich from lamin-poor regions and to rigorously extract local viscoelastic moduli from the mean-squared displacements of noninteracting particles. Our results show that human lamin B1 can, even in the absence of auxiliary proteins, form stiff and yet extremely porous networks that are well suited to provide structural strength to the nuclear lamina. Combining DIC microscopy and particle tracking allows us to relate directly the local organization of a material to its local mechanical properties, a general methodology that can be extended to living cells.

BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language.

PubMed

Rinaldi, Fabio; Ellendorff, Tilia Renate; Madan, Sumit; Clematide, Simon; van der Lek, Adrian; Mevissen, Theo; Fluck, Juliane

2016-01-01

Automatic extraction of biological network information is one of the most desired and most complex tasks in biological and medical text mining. Track 4 at BioCreative V attempts to approach this complexity using fragments of large-scale manually curated biological networks, represented in Biological Expression Language (BEL), as training and test data. BEL is an advanced knowledge representation format which has been designed to be both human readable and machine processable. The specific goal of track 4 was to evaluate text mining systems capable of automatically constructing BEL statements from given evidence text, and of retrieving evidence text for given BEL statements. Given the complexity of the task, we designed an evaluation methodology which gives credit to partially correct statements. We identified various levels of information expressed by BEL statements, such as entities, functions, relations, and introduced an evaluation framework which rewards systems capable of delivering useful BEL fragments at each of these levels. The aim of this evaluation method is to help identify the characteristics of the systems which, if combined, would be most useful for achieving the overall goal of automatically constructing causal biological networks from text. © The Author(s) 2016. Published by Oxford University Press.

Sub-0.1 μm optical track width measurement

NASA Astrophysics Data System (ADS)

Smith, Richard J.; See, Chung W.; Somekh, Mike G.; Yacoot, Andrew

2005-08-01

In this paper, we will describe a technique that combines a common path scanning optical interferometer with artificial neural networks (ANN), to perform track width measurements that are significantly beyond the capability of conventional optical systems. Artificial neural networks have been used for many different applications. In the present case, ANNs are trained using profiles of known samples obtained from the scanning interferometer. They are then applied to tracks that have not previously been exposed to the networks. This paper will discuss the impacts of various ANN configurations, and the processing of the input signal on the training of the network. The profiles of the samples, which are used as the inputs to the ANNs, are obtained with a common path scanning optical interferometer. It provides extremely repeatable measurements, with very high signal to noise ratio, both are essential for the working of the ANNs. The characteristics of the system will be described. A number of samples with line widths ranging from 60nm-3μm have been measured to test the system. The system can measure line widths down to 60nm with a standard deviation of 3nm using optical wavelength of 633nm and a system numerical aperture of 0.3. These results will be presented in detail along with a discussion of the potential of this technique.

[Emergency response management near the tracks of the public railway network: special aspects of missions connected with the German national railway system].

PubMed

Krämer, P; Aul, A; Vock, B; Frank, C

2010-11-01

Emergency response management and rescue operations concerning the railway network in Germany need special attention and implementation in several ways. The emergency response concerning the German national railway network managed by Deutsche Bahn AG is subject to various rules and regulations which have to be followed precisely. Only by following these rules and procedures is the safety of all emergency staff at the scene ensured. The German national railway network (Deutsche Bahn AG) provides its own emergency response control center, which specializes in managing its response to emergencies and dispatches an emergency response manager to the scene. This person serves as the primary Deutsche Bahn AG representative at the scene and is the only person who is allowed to earth the railway electrical power lines. This article will discuss different emergency situations concerning railway accidents and the emergency medical response to them based on a near collision with a high speed train during a rescue mission close to the railway track. Injury to personnel could only be avoided by chance and luck. The dangers and risks for rescue staff are specified. Furthermore, the article details practical guidelines for rescue operations around the German national railway track system.

Dynamic Agent Classification and Tracking Using an Ad Hoc Mobile Acoustic Sensor Network

NASA Astrophysics Data System (ADS)

Friedlander, David; Griffin, Christopher; Jacobson, Noah; Phoha, Shashi; Brooks, Richard R.

2003-12-01

Autonomous networks of sensor platforms can be designed to interact in dynamic and noisy environments to determine the occurrence of specified transient events that define the dynamic process of interest. For example, a sensor network may be used for battlefield surveillance with the purpose of detecting, identifying, and tracking enemy activity. When the number of nodes is large, human oversight and control of low-level operations is not feasible. Coordination and self-organization of multiple autonomous nodes is necessary to maintain connectivity and sensor coverage and to combine information for better understanding the dynamics of the environment. Resource conservation requires adaptive clustering in the vicinity of the event. This paper presents methods for dynamic distributed signal processing using an ad hoc mobile network of microsensors to detect, identify, and track targets in noisy environments. They seamlessly integrate data from fixed and mobile platforms and dynamically organize platforms into clusters to process local data along the trajectory of the targets. Local analysis of sensor data is used to determine a set of target attribute values and classify the target. Sensor data from a field test in the Marine base at Twentynine Palms, Calif, was analyzed using the techniques described in this paper. The results were compared to "ground truth" data obtained from GPS receivers on the vehicles.

Tracking and data relay satellite system: NASA's new spacecraft data acquisition system

NASA Astrophysics Data System (ADS)

Schneider, W. C.; Garman, A. A.

The growth in NASA's ground network complexity and cost triggered a search for an alternative. Through a lease service contract, Western Union will provide to NASA 10 years of space communications services with a Tracking and Data Relay Satellite System (TDRSS). A constellation of four operating satellites in geostationary orbit and a single ground terminal will provide complete tracking, telemetry and command service for all of NASA's Earth orbital satellites below an altitude of 12,000 km. The system is shared: two satellites will be dedicated to NASA service; a third will provide backup as a shared spare; the fourth satellite will be dedicated to Western Union's Advanced Westar commercial service. Western Union will operate the ground terminal and provide operational satellite control. NASA's Network Control Center will provide the focal point for scheduling user services and controlling the interface between TDRSS and the rest of the NASA communications network, project control centers and data processing facilities. TDRSS single access user spacecraft data systems should be designed for efficient time shared data relay support. Reimbursement policy and rate structure for non-NASA users are currently being developed.

A hybrid mobile-based patient location tracking system for personal healthcare applications.

PubMed

Chew, S H; Chong, P A; Gunawan, E; Goh, K W; Kim, Y; Soh, C B

2006-01-01

In the next generation of Infocommunications, mobile Internet-enabled devices and third generation mobile communication networks have become reality, location based services (LBS) are expected to be a major area of growth. Providing information, content and services through positioning technologies forms the platform for new services for users and developers, as well as creating new revenue channels for service providers. These crucial advances in location based services have opened up new opportunities in real time patient tracking for personal healthcare applications. In this paper, a hybrid mobile-based location technique using the global positioning system (GPS) and cellular mobile network infrastructure is employed to provide the location tracking capability. This function will be integrated into the patient location tracking system (PLTS) to assist caregivers or family members in locating patients such as elderly or dependents when required, especially in emergencies. The capability of this PLTS is demonstrated through a series of location detection tests conducted over different operating conditions. Although the model is at its initial stage of development, it has shown relatively good accuracy for position tracking and potential of using integrated wireless technology to enhance the existing personal healthcare communication system through location based services.

Image Tracking for the High Similarity Drug Tablets Based on Light Intensity Reflective Energy and Artificial Neural Network

PubMed Central

Liang, Zhongwei; Zhou, Liang; Liu, Xiaochu; Wang, Xiaogang

2014-01-01

It is obvious that tablet image tracking exerts a notable influence on the efficiency and reliability of high-speed drug mass production, and, simultaneously, it also emerges as a big difficult problem and targeted focus during production monitoring in recent years, due to the high similarity shape and random position distribution of those objectives to be searched for. For the purpose of tracking tablets accurately in random distribution, through using surface fitting approach and transitional vector determination, the calibrated surface of light intensity reflective energy can be established, describing the shape topology and topography details of objective tablet. On this basis, the mathematical properties of these established surfaces have been proposed, and thereafter artificial neural network (ANN) has been employed for classifying those moving targeted tablets by recognizing their different surface properties; therefore, the instantaneous coordinate positions of those drug tablets on one image frame can then be determined. By repeating identical pattern recognition on the next image frame, the real-time movements of objective tablet templates were successfully tracked in sequence. This paper provides reliable references and new research ideas for the real-time objective tracking in the case of drug production practices. PMID:25143781

Model-Free Optimal Tracking Control via Critic-Only Q-Learning.

PubMed

Luo, Biao; Liu, Derong; Huang, Tingwen; Wang, Ding

2016-10-01

Model-free control is an important and promising topic in control fields, which has attracted extensive attention in the past few years. In this paper, we aim to solve the model-free optimal tracking control problem of nonaffine nonlinear discrete-time systems. A critic-only Q-learning (CoQL) method is developed, which learns the optimal tracking control from real system data, and thus avoids solving the tracking Hamilton-Jacobi-Bellman equation. First, the Q-learning algorithm is proposed based on the augmented system, and its convergence is established. Using only one neural network for approximating the Q-function, the CoQL method is developed to implement the Q-learning algorithm. Furthermore, the convergence of the CoQL method is proved with the consideration of neural network approximation error. With the convergent Q-function obtained from the CoQL method, the adaptive optimal tracking control is designed based on the gradient descent scheme. Finally, the effectiveness of the developed CoQL method is demonstrated through simulation studies. The developed CoQL method learns with off-policy data and implements with a critic-only structure, thus it is easy to realize and overcome the inadequate exploration problem.

EPANET 2 USERS MANUAL

EPA Science Inventory

EPANET is a computer program that performs extended period simulation of hydraulic and water quality behavior within pressurized pipe networks. A network consists of pipes, nodes (pipe junctions), pumps, valves and storage tanks or reservoirs. EPANET tracks the flow of water in e...

EPANET VERSION 2.0

EPA Science Inventory

EPANET is a Windows program that performs extended period simulation of hydraulic and water-quality behavior within pressurized pipe networks. A network can consist of pipes, nodes (pipe junctions), pumps, valves and storage tanks or reservoirs. EPANET tracks the flow of water in...

Inertial Motion Tracking for Inserting Humans into a Networked Synthetic Environment

DTIC Science & Technology

2007-08-31

tracking methods. One method requires markers on the tracked buman body, and other method does not use nmkers. OPTOTRAK from Northem Digital Inc. is a...of using multicasting protocols. Unfortunately, most routers on the Internet are not configured for multicasting. A technique called tunneling is...used to overcome this problem. Tunneling is a software solution that m s on the end point routerslcomputers and allows multicast packets to traverse

The Use of Technology in Participant Tracking and Study Retention: Lessons Learned From a Clinical Trials Network Study.

PubMed

Mitchell, Shannon Gwin; Schwartz, Robert P; Alvanzo, Anika A H; Weisman, Monique S; Kyle, Tiffany L; Turrigiano, Eva M; Gibson, Martha L; Perez, Livangelie; McClure, Erin A; Clingerman, Sara; Froias, Autumn; Shandera, Danielle R; Walker, Robrina; Babcock, Dean L; Bailey, Genie L; Miele, Gloria M; Kunkel, Lynn E; Norton, Michael; Stitzer, Maxine L

2015-01-01

The growing use of newer communication and Internet technologies, even among low-income and transient populations, require research staff to update their outreach strategies to ensure high follow-up and participant retention rates. This paper presents the views of research assistants on the use of cell phones and the Internet to track participants in a multisite randomized trial of substance use disorder treatment. Preinterview questionnaires exploring tracking and other study-related activities were collected from 21 research staff across the 10 participating US sites. Data were then used to construct a semistructured interview guide that, in turn, was used to interview 12 of the same staff members. The questionnaires and interview data were entered in Atlas.ti and analyzed for emergent themes related to the use of technology for participant-tracking purposes. Study staff reported that most participants had cell phones, despite having unstable physical addresses and landlines. The incoming call feature of most cell phones was useful for participants and research staff alike, and texting proved to have additional benefits. However, reliance on participants' cell phones also proved problematic. Even homeless participants were found to have access to the Internet through public libraries and could respond to study staff e-mails. Some study sites opened generic social media accounts, through which study staff sent private messages to participants. However, the institutional review board (IRB) approval process for tracking participants using social media at some sites was prohibitively lengthy. Internet searches through Google, national paid databases, obituaries, and judiciary Web sites were also helpful tools. Research staff perceive that cell phones, Internet searches, and social networking sites were effective tools to achieve high follow-up rates in drug abuse research. Studies should incorporate cell phone, texting, and social network Web site information on locator forms; obtain IRB approval for contacting participants using social networking Web sites; and include Web searches, texting, and the use of social media in staff training as standard operating procedures.

The Tracking & Data Relay Satellite System. The New Space Network.

ERIC Educational Resources Information Center

Froehlich, Walter

This publication describes the giant-capacity space communications installation called the "Tracking and Data Relay Satellite System" (TDRSS). Chapters include: (1) "A New Communications Bridge to Orbit" (illustrating what it is and how it looks); (2) "TDRSS Goes to Work" (describing how it functions); (3) "The…

Application of GPS tracking techniques to orbit determination for TDRS

NASA Technical Reports Server (NTRS)

Haines, B. J.; Lichten, S. M.; Malla, R. P.; Wu, S. C.

1993-01-01

In this paper, we evaluate two fundamentally different approaches to TDRS orbit determination utilizing Global Positioning System (GPS) technology and GPS-related techniques. In the first, a GPS flight receiver is deployed on the TDRSS spacecraft. The TDRS ephemerides are determined using direct ranging to the GPS spacecraft, and no ground network is required. In the second approach, the TDRSS spacecraft broadcast a suitable beacon signal, permitting the simultaneous tracking of GPS and TDRSS satellites from a small ground network. Both strategies can be designed to meet future operational requirements for TDRS-2 orbit determination.

Tracking and data systems support for the Helios project. Volume 1: Project development through end of mission, phase 2

NASA Technical Reports Server (NTRS)

Goodwin, P. S.; Traxler, M. R.; Meeks, W. G.; Flanagan, F. M.

1976-01-01

The overall evolution of the Helios Project is summarized from its conception through to the completion of the Helios-1 mission phase 2. Beginning with the project objectives and concluding with the Helios-1 spacecraft entering its first superior conjunction (end of mission phase 2), descriptions of the project, the mission and its phases, international management and interfaces, and Deep Space Network-spacecraft engineering development in telemetry, tracking, and command systems to ensure compatibility between the U.S. Deep Space Network and the German-built spacecraft are included.

An alternative way to track the hot money in turbulent times

NASA Astrophysics Data System (ADS)

Sensoy, Ahmet

2015-02-01

During recent years, networks have proven to be an efficient way to characterize and investigate a wide range of complex financial systems. In this study, we first obtain the dynamic conditional correlations between filtered exchange rates (against US dollar) of several countries and introduce a time-varying threshold correlation level to define dynamic strong correlations between these exchange rates. Then, using evolving networks obtained from strong correlations, we propose an alternative approach to track the hot money in turbulent times. The approach is demonstrated for the time period including the financial turmoil of 2008. Other applications are also discussed.

Using Multiple Space Assests with In-Situ Measurements to Track Flooding in Thailand

NASA Technical Reports Server (NTRS)

Chien, Steve; Doubleday, Joshua; Mclaren, David; Tran, Daniel; Khunboa, Chatchai; Leelapatra, Watis; Pergamon, Vichain; Tanpipat, Veerachai; Chitradon, Royal; Boonya-aroonnet, Surajate;

INDIRECT INTELLIGENT SLIDING MODE CONTROL OF A SHAPE MEMORY ALLOY ACTUATED FLEXIBLE BEAM USING HYSTERETIC RECURRENT NEURAL NETWORKS.

PubMed

Hannen, Jennifer C; Crews, John H; Buckner, Gregory D

2012-08-01

This paper introduces an indirect intelligent sliding mode controller (IISMC) for shape memory alloy (SMA) actuators, specifically a flexible beam deflected by a single offset SMA tendon. The controller manipulates applied voltage, which alters SMA tendon temperature to track reference bending angles. A hysteretic recurrent neural network (HRNN) captures the nonlinear, hysteretic relationship between SMA temperature and bending angle. The variable structure control strategy provides robustness to model uncertainties and parameter variations, while effectively compensating for system nonlinearities, achieving superior tracking compared to an optimized PI controller.

Quantized Iterative Learning Consensus Tracking of Digital Networks With Limited Information Communication.

PubMed

Xiong, Wenjun; Yu, Xinghuo; Chen, Yao; Gao, Jie

2017-06-01

This brief investigates the quantized iterative learning problem for digital networks with time-varying topologies. The information is first encoded as symbolic data and then transmitted. After the data are received, a decoder is used by the receiver to get an estimate of the sender's state. Iterative learning quantized communication is considered in the process of encoding and decoding. A sufficient condition is then presented to achieve the consensus tracking problem in a finite interval using the quantized iterative learning controllers. Finally, simulation results are given to illustrate the usefulness of the developed criterion.

Tracking near-surface atmospheric conditions using an infrasound network.

PubMed

Marcillo, O; Johnson, J B

2010-07-01

Continuous volcanic infrasound signal was recorded on a three-microphone network at Kilauea in July 2008 and inverted for near-surface horizontal winds. Inter-station phase delays, determined by signal cross-correlation, vary by up to 4% and are attributable to variable atmospheric conditions. The results suggest two predominant weather regimes during the study period: (1) 6-9 m/s easterly trade winds and (2) lower-intensity 2-5 m/s mountain breezes from Mauna Loa. The results demonstrate the potential of using infrasound for tracking local averaged meteorological conditions, which has implications for modeling plume dispersal and quantifying gas flux.

The Telecommunications and Data Acquisition Report. [Deep Space Network

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1986-01-01

This publication, one of a series formerly titled The Deep Space Network Progress Report, documents DSN progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations. In addition, developments in Earth-based radio technology as applied to geodynamics, astrophysics and the radio search for extraterrestrial intelligence are reported.

The deep space network

NASA Technical Reports Server (NTRS)

1975-01-01

The objectives, functions, and organization of the Deep Space Network are summarized along with deep space station, ground communication, and network operations control capabilities. Mission support of ongoing planetary/interplanetary flight projects is discussed with emphasis on Viking orbiter radio frequency compatibility tests, the Pioneer Venus orbiter mission, and Helios-1 mission status and operations. Progress is also reported in tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations.

The Deep Space Network

NASA Technical Reports Server (NTRS)

1974-01-01

The objectives, functions, and organization, of the Deep Space Network are summarized. Deep Space stations, ground communications, and network operations control capabilities are described. The network is designed for two-way communications with unmanned spacecraft traveling approximately 1600 km from earth to the farthest planets in the solar system. It has provided tracking and data acquisition support for the following projects: Ranger, Surveyor, Mariner, Pioneer, Apollo, Helios, Viking, and the Lunar Orbiter.

Operational aspects of CASA UNO '88-The first large scale international GPS geodetic network

NASA Technical Reports Server (NTRS)

Neilan, Ruth E.; Dixon, T. H.; Meehan, Thomas K.; Melbourne, William G.; Scheid, John A.; Kellogg, J. N.; Stowell, J. L.

1989-01-01

For three weeks, from January 18 to February 5, 1988, scientists and engineers from 13 countries and 30 international agencies and institutions cooperated in the most extensive GPS (Global Positioning System) field campaign, and the largest geodynamics experiment, in the world to date. This collaborative eperiment concentrated GPS receivers in Central and South America. The predicted rates of motions are on the order of 5-10 cm/yr. Global coverage of GPS observations spanned 220 deg of longitude and 125 deg of latitude using a total of 43 GPS receivers. The experiment was the first civilian effort at implementing an extended international GPS satellite tracking network. Covariance analyses incorporating the extended tracking network predicted significant improvement in precise orbit determination, allowing accurate long-baseline geodesy in the science areas.

Prediction-based Dynamic Energy Management in Wireless Sensor Networks

PubMed Central

Wang, Xue; Ma, Jun-Jie; Wang, Sheng; Bi, Dao-Wei

2007-01-01

Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a target state, which was adopted to awaken wireless sensor nodes so that their sleep time was prolonged. With the distributed computing capability of nodes, an optimization approach of distributed genetic algorithm and simulated annealing was proposed to minimize the energy consumption of measurement. Considering the application of target tracking, we implemented target position prediction, node sleep scheduling and optimal sensing node selection. Moreover, a routing scheme of forwarding nodes was presented to achieve extra energy conservation. Experimental results of target tracking verified that energy-efficiency is enhanced by prediction-based dynamic energy management.

Power harvesting for railroad track safety enhancement using vertical track displacement

NASA Astrophysics Data System (ADS)

Nelson, Carl A.; Platt, Stephen R.; Hansen, Sean E.; Fateh, Mahmood

2009-03-01

A significant portion of railroad infrastructure exists in areas that are relatively remote. Railroad crossings in these areas are typically only marked with reflective signage and do not have warning light systems or crossbars due to the cost of electrical infrastructure. Distributed sensor networks used for railroad track health monitoring applications would be useful in these areas, but the same limitation regarding electrical infrastructure exists. This motivates the search for a long-term, low-maintenance power supply solution for remote railroad deployment. This paper describes the development of a mechanical device for harvesting mechanical power from passing railcar traffic that can be used to supply electrical power to warning light systems at crossings and to remote networks of sensors via rechargeable batteries. The device is mounted to and spans two rail ties such that it directly harnesses the vertical displacement of the rail and attached ties and translates the linear motion into rotational motion. The rotational motion is amplified and mechanically rectified to rotate a PMDC generator that charges a system of batteries. A prototype was built and tested in a laboratory setting for verifying functionality of the design. Results indicate power production capabilities on the order of 10 W per device in its current form. This is sufficient for illuminating high-efficiency LED lights at a railroad crossing or for powering track-health sensor networks.

DISCRETE VOLUME-ELEMENT METHOD FOR NETWORK WATER- QUALITY MODELS

EPA Science Inventory

An explicit dynamic water-quality modeling algorithm is developed for tracking dissolved substances in water-distribution networks. The algorithm is based on a mass-balance relation within pipes that considers both advective transport and reaction kinetics. Complete mixing of m...

Multi-Gigabit Free-Space Optical Data Communication and Network System

DTIC Science & Technology

2016-04-01

IR), Ultraviolet ( UV ), Laser Transceiver, Adaptive Beam Tracking, Electronic Attack (EA), Cyber Attack, Multipoint-to-Multipoint Network, Adaptive...FileName.pptx Free Space Optical Datalink Timeline Phase 1 Point-to-point demonstration 2012 Future Adaptive optic & Quantum Cascade Laser

Role of TDRSS in tracking and data acquisition

NASA Technical Reports Server (NTRS)

Spearing, R. E.

1980-01-01

The integration and operation of the Tracking Data Relay Satellite System (TDRSS) into the NASA Communications Network (NASCOM) equipment and services is described. The system concept employs spacecraft in geosynchronous orbit, operating as communications front-ends, and a single ground terminal, which provides primary tracking and data acquisition services for earth-orbiting user satellites and for the Space Shuttle. The TDRSS system is further characterized by real-time throughput of user data and a high degree of automation.

Statistical-Mechanics-Inspired Optimization of Sensor Field Configuration for Detection of Mobile Targets (PREPRINT)

DTIC Science & Technology

2010-11-01

pected target motion. Along this line, Wettergren [5] analyzed the performance of the track - before - detect schemes for the sensor networks. Furthermore...dressed by Baumgartner and Ferrari [11] for the reorganization of the sensor field to achieve the maximum coverage. The track - before - detect -based optimal...confirming a target. In accordance with the track - before - detect paradigm [4], a moving target is detected if the kd (typically kd = 3 or 4) sensors detect

FINAL TECHNICAL REPORT: Underwater Active Acoustic Monitoring Network For Marine And Hydrokinetic Energy Projects

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stein, Peter J.; Edson, Patrick L.

2013-12-20

This project saw the completion of the design and development of a second generation, high frequency (90-120 kHz) Subsurface-Threat Detection Sonar Network (SDSN). The system was deployed, operated, and tested in Cobscook Bay, Maine near the site the Ocean Renewable Power Company TidGen™ power unit. This effort resulted in a very successful demonstration of the SDSN detection, tracking, localization, and classification capabilities in a high current, MHK environment as measured by results from the detection and tracking trials in Cobscook Bay. The new high frequency node, designed to operate outside the hearing range of a subset of marine mammals, wasmore » shown to detect and track objects of marine mammal-like target strength to ranges of approximately 500 meters. This performance range results in the SDSN system tracking objects for a significant duration - on the order of minutes - even in a tidal flow of 5-7 knots, potentially allowing time for MHK system or operator decision-making if marine mammals are present. Having demonstrated detection and tracking of synthetic targets with target strengths similar to some marine mammals, the primary hurdle to eventual automated monitoring is a dataset of actual marine mammal kinematic behavior and modifying the tracking algorithms and parameters which are currently tuned to human diver kinematics and classification.« less

A review of GPS-based tracking techniques for TDRS orbit determination

NASA Technical Reports Server (NTRS)

Haines, B. J.; Lichten, S. M.; Malla, R. P.; Wu, S.-C.

1993-01-01

This article evaluates two fundamentally different approaches to the Tracking and Data Relay Satellite (TDRS) orbit determination utilizing Global Positioning System (GPS) technology and GPS-related techniques. In the first, a GPS flight receiver is deployed on the TDRS. The TDRS ephemerides are determined using direct ranging to the GPS spacecraft, and no ground network is required. In the second approach, the TDRS's broadcast a suitable beacon signal, permitting the simultaneous tracking of GPS and Tracking and Data Relay Satellite System satellites by ground receivers. Both strategies can be designed to meet future operational requirements for TDRS-II orbit determination.

Learned filters for object detection in multi-object visual tracking

NASA Astrophysics Data System (ADS)

Stamatescu, Victor; Wong, Sebastien; McDonnell, Mark D.; Kearney, David

2016-05-01

We investigate the application of learned convolutional filters in multi-object visual tracking. The filters were learned in both a supervised and unsupervised manner from image data using artificial neural networks. This work follows recent results in the field of machine learning that demonstrate the use learned filters for enhanced object detection and classification. Here we employ a track-before-detect approach to multi-object tracking, where tracking guides the detection process. The object detection provides a probabilistic input image calculated by selecting from features obtained using banks of generative or discriminative learned filters. We present a systematic evaluation of these convolutional filters using a real-world data set that examines their performance as generic object detectors.

Performance Evaluation of a Field Programmable Gate Array-Based System for Detecting and Tracking Peer-to-Peer Protocols on a Gigabit Ethernet Network

DTIC Science & Technology

2010-06-01

Ron’s Code 4 . . . . . . . . . . . . . . . . . . . 18 2.3.3 Virtual Private Network and Secure Shell Tunnels 19 2.3.4 Darknets ...created using Iodine. 2.2 Analyzing and Classifying Network Traffic Before the advent of Darknets and anonymizers like Tor (see Section 2.3), ana... darknets , and the Tor network. 2.3.1 Byte Padding. Byte padding is the most primitive obfuscation method used to hide payloads in network traffic. When byte

Complementary and alternative medicine for Duchenne and Becker muscular dystrophies: characteristics of users and caregivers.

PubMed

Zhu, Yong; Romitti, Paul A; Conway, Kristin M; Andrews, Jennifer; Liu, Ke; Meaney, F John; Street, Natalie; Puzhankara, Soman; Druschel, Charlotte M; Matthews, Dennis J

2014-07-01

Complementary and alternative medicine is frequently used in the management of chronic pediatric diseases, but little is known about its use by those with Duchenne or Becker muscular dystrophy. Complementary and alternative medicine use by male patients with Duchenne or Becker muscular dystrophy and associations with characteristics of male patients and their caregivers were examined through interviews with 362 primary caregivers identified from the Muscular Dystrophy Surveillance, Tracking, and Research Network. Overall, 272 of the 362 (75.1%) primary caregivers reported that they had used any complementary and alternative medicine for the oldest Muscular Dystrophy Surveillance, Tracking, and Research Network male in their family. The most commonly reported therapies were from the mind-body medicine domain (61.0%) followed by those from the biologically based practice (39.2%), manipulative and body-based practice (29.3%), and whole medical system (6.9%) domains. Aquatherapy, prayer and/or blessing, special diet, and massage were the most frequently used therapies. Compared with nonusers, male patients who used any therapy were more likely to have an early onset of symptoms and use a wheel chair; their caregivers were more likely to be non-Hispanic white. Among domains, associations were observed with caregiver education and family income (mind-body medicines [excluding prayer and/or blessing only] and whole medical systems) and Muscular Dystrophy Surveillance, Tracking, and Research Network site (biologically based practices and mind-body medicines [excluding prayer and/or blessing only]). Complementary and alternative medicine use was common in the management of Duchenne and Becker muscular dystrophies among Muscular Dystrophy Surveillance, Tracking, and Research Network males. This widespread use suggests further study to evaluate the efficacy of integrating complementary and alternative medicine into treatment regimens for Duchenne and Becker muscular dystrophies. Copyright © 2014 Elsevier Inc. All rights reserved.

Telecommunications and data acquisition

NASA Technical Reports Server (NTRS)

Renzetti, N. A. (Editor)

1981-01-01

Deep Space Network progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is reported. In addition, developments in Earth based radio technology as applied to geodynamics, astrophysics, and the radio search for extraterrestrial intelligence are reported.

The deep space network, volume 19

NASA Technical Reports Server (NTRS)

1974-01-01

The progress is reported in the DSN for Nov. and Dec. 1973. Research is described for the following areas: functions and facilities, mission support for flight projects, tracking and ground-based navigation, spacecraft/ground communication, network control and operations technology, and deep space stations.

Fluctuations and Noise in Stochastic Spread of Respiratory Infection Epidemics in Social Networks

NASA Astrophysics Data System (ADS)

Yulmetyev, Renat; Emelyanova, Natalya; Demin, Sergey; Gafarov, Fail; Hänggi, Peter; Yulmetyeva, Dinara

2003-05-01

For the analysis of epidemic and disease dynamics complexity, it is necessary to understand the basic principles and notions of its spreading in long-time memory media. Here we considering the problem from a theoretical and practical viewpoint, presenting the quantitative evidence confirming the existence of stochastic long-range memory and robust chaos in a real time series of respiratory infections of human upper respiratory track. In this work we present a new statistical method of analyzing the spread of grippe and acute respiratory track infections epidemic process of human upper respiratory track by means of the theory of discrete non-Markov stochastic processes. We use the results of our recent theory (Phys. Rev. E 65, 046107 (2002)) for the study of statistical effects of memory in real data series, describing the epidemic dynamics of human acute respiratory track infections and grippe. The obtained results testify to an opportunity of the strict quantitative description of the regular and stochastic components in epidemic dynamics of social networks with a view to time discreteness and effects of statistical memory.

A Database of Tornado Events as Perceived by the USArray Transportable Array Network

NASA Astrophysics Data System (ADS)

Tytell, J. E.; Vernon, F.; Reyes, J. C.

2015-12-01

Over the course of the deployment of Earthscope's USArray Transportable Array (TA) network there have numerous tornado events that have occurred within the changing footprint of its network. The Array Network Facility based in San Diego, California, has compiled a database of these tornado events based on data provided by the NOAA Storm Prediction Center (SPC). The SPC data itself consists of parameters such as start-end point track data for each event, maximum EF intensities, and maximum track widths. Our database is Antelope driven and combines these data from the SPC with detailed station information from the TA network. We are now able to list all available TA stations during any specific tornado event date and also provide a single calculated "nearest" TA station per individual tornado event. We aim to provide this database as a starting resource for those with an interest in investigating tornado signatures within surface pressure and seismic response data. On a larger scale, the database may be of particular interest to the infrasound research community

A cloud-based forensics tracking scheme for online social network clients.

PubMed

Lin, Feng-Yu; Huang, Chien-Cheng; Chang, Pei-Ying

2015-10-01

In recent years, with significant changes in the communication modes, most users are diverted to cloud-based applications, especially online social networks (OSNs), which applications are mostly hosted on the outside and available to criminals, enabling them to impede criminal investigations and intelligence gathering. In the virtual world, how the Law Enforcement Agency (LEA) identifies the "actual" identity of criminal suspects, and their geolocation in social networks, is a major challenge to current digital investigation. In view of this, this paper proposes a scheme, based on the concepts of IP location and network forensics, which aims to develop forensics tracking on OSNs. According to our empirical analysis, the proposed mechanism can instantly trace the "physical location" of a targeted service resource identifier (SRI), when the target client is using online social network applications (Facebook, Twitter, etc.), and can analyze the probable target client "identity" associatively. To the best of our knowledge, this is the first individualized location method and architecture developed and evaluated in OSNs. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Kennedy Space Center network documentation system

NASA Technical Reports Server (NTRS)

Lohne, William E.; Schuerger, Charles L.

1995-01-01

The Kennedy Space Center Network Documentation System (KSC NDS) is being designed and implemented by NASA and the KSC contractor organizations to provide a means of network tracking, configuration, and control. Currently, a variety of host and client platforms are in use as a result of each organization having established its own network documentation system. The solution is to incorporate as many existing 'systems' as possible in the effort to consolidate and standardize KSC-wide documentation.

Reconfigurable Flight Control Design using a Robust Servo LQR and Radial Basis Function Neural Networks

NASA Technical Reports Server (NTRS)

Burken, John J.

2005-01-01

This viewgraph presentation reviews the use of a Robust Servo Linear Quadratic Regulator (LQR) and a Radial Basis Function (RBF) Neural Network in reconfigurable flight control designs in adaptation to a aircraft part failure. The method uses a robust LQR servomechanism design with model Reference adaptive control, and RBF neural networks. During the failure the LQR servomechanism behaved well, and using the neural networks improved the tracking.

The BEL information extraction workflow (BELIEF): evaluation in the BioCreative V BEL and IAT track

PubMed Central

Madan, Sumit; Hodapp, Sven; Senger, Philipp; Ansari, Sam; Szostak, Justyna; Hoeng, Julia; Peitsch, Manuel; Fluck, Juliane

2016-01-01

Network-based approaches have become extremely important in systems biology to achieve a better understanding of biological mechanisms. For network representation, the Biological Expression Language (BEL) is well designed to collate findings from the scientific literature into biological network models. To facilitate encoding and biocuration of such findings in BEL, a BEL Information Extraction Workflow (BELIEF) was developed. BELIEF provides a web-based curation interface, the BELIEF Dashboard, that incorporates text mining techniques to support the biocurator in the generation of BEL networks. The underlying UIMA-based text mining pipeline (BELIEF Pipeline) uses several named entity recognition processes and relationship extraction methods to detect concepts and BEL relationships in literature. The BELIEF Dashboard allows easy curation of the automatically generated BEL statements and their context annotations. Resulting BEL statements and their context annotations can be syntactically and semantically verified to ensure consistency in the BEL network. In summary, the workflow supports experts in different stages of systems biology network building. Based on the BioCreative V BEL track evaluation, we show that the BELIEF Pipeline automatically extracts relationships with an F-score of 36.4% and fully correct statements can be obtained with an F-score of 30.8%. Participation in the BioCreative V Interactive task (IAT) track with BELIEF revealed a systems usability scale (SUS) of 67. Considering the complexity of the task for new users—learning BEL, working with a completely new interface, and performing complex curation—a score so close to the overall SUS average highlights the usability of BELIEF. Database URL: BELIEF is available at http://www.scaiview.com/belief/ PMID:27694210

The BEL information extraction workflow (BELIEF): evaluation in the BioCreative V BEL and IAT track.

PubMed

Madan, Sumit; Hodapp, Sven; Senger, Philipp; Ansari, Sam; Szostak, Justyna; Hoeng, Julia; Peitsch, Manuel; Fluck, Juliane

2016-01-01

Network-based approaches have become extremely important in systems biology to achieve a better understanding of biological mechanisms. For network representation, the Biological Expression Language (BEL) is well designed to collate findings from the scientific literature into biological network models. To facilitate encoding and biocuration of such findings in BEL, a BEL Information Extraction Workflow (BELIEF) was developed. BELIEF provides a web-based curation interface, the BELIEF Dashboard, that incorporates text mining techniques to support the biocurator in the generation of BEL networks. The underlying UIMA-based text mining pipeline (BELIEF Pipeline) uses several named entity recognition processes and relationship extraction methods to detect concepts and BEL relationships in literature. The BELIEF Dashboard allows easy curation of the automatically generated BEL statements and their context annotations. Resulting BEL statements and their context annotations can be syntactically and semantically verified to ensure consistency in the BEL network. In summary, the workflow supports experts in different stages of systems biology network building. Based on the BioCreative V BEL track evaluation, we show that the BELIEF Pipeline automatically extracts relationships with an F-score of 36.4% and fully correct statements can be obtained with an F-score of 30.8%. Participation in the BioCreative V Interactive task (IAT) track with BELIEF revealed a systems usability scale (SUS) of 67. Considering the complexity of the task for new users-learning BEL, working with a completely new interface, and performing complex curation-a score so close to the overall SUS average highlights the usability of BELIEF.Database URL: BELIEF is available at http://www.scaiview.com/belief/. © The Author(s) 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Reconfigurable Control with Neural Network Augmentation for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.; Williams-Hayes, Peggy; Kaneshige, John T.; Stachowiak, Susan J.

2006-01-01

Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

Adaptive Control Using Neural Network Augmentation for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.; Williams-Hayes, Peggy; Karneshige, J. T.; Stachowiak, Susan J.

2006-01-01

Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

Cortical Circuit for Binding Object Identity and Location During Multiple-Object Tracking

PubMed Central

Nummenmaa, Lauri; Oksama, Lauri; Glerean, Erico; Hyönä, Jukka

2017-01-01

Abstract Sustained multifocal attention for moving targets requires binding object identities with their locations. The brain mechanisms of identity-location binding during attentive tracking have remained unresolved. In 2 functional magnetic resonance imaging experiments, we measured participants’ hemodynamic activity during attentive tracking of multiple objects with equivalent (multiple-object tracking) versus distinct (multiple identity tracking, MIT) identities. Task load was manipulated parametrically. Both tasks activated large frontoparietal circuits. MIT led to significantly increased activity in frontoparietal and temporal systems subserving object recognition and working memory. These effects were replicated when eye movements were prohibited. MIT was associated with significantly increased functional connectivity between lateral temporal and frontal and parietal regions. We propose that coordinated activity of this network subserves identity-location binding during attentive tracking. PMID:27913430

Neural network-based optimal adaptive output feedback control of a helicopter UAV.

PubMed

Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani

2013-07-01

Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.

Application of inertial instruments for DSN antenna pointing and tracking

NASA Technical Reports Server (NTRS)

Eldred, D. B.; Nerheim, N. M.; Holmes, K. G.

1990-01-01

The feasibility of using inertial instruments to determine the pointing attitude of the NASA Deep Space Network antennas is examined. The objective is to obtain 1 mdeg pointing knowledge in both blind pointing and tracking modes to facilitate operation of the Deep Space Network 70 m antennas at 32 GHz. A measurement system employing accelerometers, an inclinometer, and optical gyroscopes is proposed. The initial pointing attitude is established by determining the direction of the local gravity vector using the accelerometers and the inclinometer, and the Earth's spin axis using the gyroscopes. Pointing during long-term tracking is maintained by integrating the gyroscope rates and augmenting these measurements with knowledge of the local gravity vector. A minimum-variance estimator is used to combine measurements to obtain the antenna pointing attitude. A key feature of the algorithm is its ability to recalibrate accelerometer parameters during operation. A survey of available inertial instrument technologies is also given.

Satellite tracking and earth dynamics research programs

NASA Technical Reports Server (NTRS)

1982-01-01

The SAO laser site in Arequipa continued routine operations throughout the reporting period except for the months of March and April when upgrading was underway. The laser in Orroral Valley was operational through March. Together with the cooperating stations in Wettzell, Grasse, Kootwikj, San Fernando, Helwan, and Metsahove the laser stations obtained a total of 37,099 quick-look observations on 978 passes of BE-C, Starlette, and LAGEOS. The Network continued to track LAGEOS at highest priority for polar motion and Earth rotation studies, and for other geophysical investigations, including crustal dynamics, Earth and ocean tides, and the general development of precision orbit determination. The Network performed regular tracking of BE-C and Starlette for refined determinations of station coordinate and the Earth's gravity field and for studies of solid earth dynamics. Monthly statistics of the passes and points are given by station and by satellite.

Satellite-tracking and Earth dynamics research programs

NASA Technical Reports Server (NTRS)

1982-01-01

The activities carried out by the Smithsonian Astrophysical Observatory (SAO) are described. The SAO network continued to track LAGEOS at highest priority for polar motion and Earth rotation studies, and for other geophysical investigations, including crustal dynamics, Earth and ocean tides, and the general development of precision orbit determination. The network performed regular tracking of several other retroreflector satellites including GEOS-1, GEOS-3, BE-C, and Starlette for refined determinations of station coordinates and the Earth's gravity field and for studies of solid Earth dynamics. A major program in laser upgrading continued to improve ranging accuracy and data yield. This program includes an increase in pulse repetition rate from 8 ppm to 30 ppm, a reduction in laser pulse width from 6 nsec to 2 to 3 nsec, improvements in the photoreceiver and the electronics to improve daylight ranging, and an analog pulse detection system to improve range noise and accuracy. Data processing hardware and software are discussed.

Tracking and data relay satellite system (TDRSS) capabilities

NASA Astrophysics Data System (ADS)

Spearing, R. E.

1985-10-01

The Tracking and Data Relay Satellite System (TDRSS) is the latest implementation to tracking and data acquisition network for near-earth orbiting satellite support designed to meet the requirements of the current and projected (to the year 2000) satellite user community. The TDRSS consists of a space segment (SS) and a ground segment (GS) that fit within NASA's Space Network (SN) complex controlled at the Goddard Space Flight Center. The SS currently employs a single satellite, TDRS-1, with two additional satellites to be deployed in January 1986 and July 1986. The GS contains the communications and equipment required to manage the three TDR satellites and to transmit and receive information to and from TDRSS user satellites. Diagrams and tables illustrating the TDRSS signal characteristics, the situation of TDRSS within the SN, the SN operations and element interrelationships, as well as future plans for new missions are included.

Tracking and data relay satellite system (TDRSS) capabilities

NASA Technical Reports Server (NTRS)

Spearing, R. E.

1985-01-01

The Tracking and Data Relay Satellite System (TDRSS) is the latest implementation to tracking and data acquisition network for near-earth orbiting satellite support designed to meet the requirements of the current and projected (to the year 2000) satellite user community. The TDRSS consists of a space segment (SS) and a ground segment (GS) that fit within NASA's Space Network (SN) complex controlled at the Goddard Space Flight Center. The SS currently employs a single satellite, TDRS-1, with two additional satellites to be deployed in January 1986 and July 1986. The GS contains the communications and equipment required to manage the three TDR satellites and to transmit and receive information to and from TDRSS user satellites. Diagrams and tables illustrating the TDRSS signal characteristics, the situation of TDRSS within the SN, the SN operations and element interrelationships, as well as future plans for new missions are included.

Distributed Adaptive Neural Network Output Tracking of Leader-Following High-Order Stochastic Nonlinear Multiagent Systems With Unknown Dead-Zone Input.

PubMed

Hua, Changchun; Zhang, Liuliu; Guan, Xinping

2017-01-01

This paper studies the problem of distributed output tracking consensus control for a class of high-order stochastic nonlinear multiagent systems with unknown nonlinear dead-zone under a directed graph topology. The adaptive neural networks are used to approximate the unknown nonlinear functions and a new inequality is used to deal with the completely unknown dead-zone input. Then, we design the controllers based on backstepping method and the dynamic surface control technique. It is strictly proved that the resulting closed-loop system is stable in probability in the sense of semiglobally uniform ultimate boundedness and the tracking errors between the leader and the followers approach to a small residual set based on Lyapunov stability theory. Finally, two simulation examples are presented to show the effectiveness and the advantages of the proposed techniques.

An adaptive trajectory tracking control of four rotor hover vehicle using extended normalized radial basis function network

NASA Astrophysics Data System (ADS)

ul Amin, Rooh; Aijun, Li; Khan, Muhammad Umer; Shamshirband, Shahaboddin; Kamsin, Amirrudin

2017-01-01

In this paper, an adaptive trajectory tracking controller based on extended normalized radial basis function network (ENRBFN) is proposed for 3-degree-of-freedom four rotor hover vehicle subjected to external disturbance i.e. wind turbulence. Mathematical model of four rotor hover system is developed using equations of motions and a new computational intelligence based technique ENRBFN is introduced to approximate the unmodeled dynamics of the hover vehicle. The adaptive controller based on the Lyapunov stability approach is designed to achieve tracking of the desired attitude angles of four rotor hover vehicle in the presence of wind turbulence. The adaptive weight update based on the Levenberg-Marquardt algorithm is used to avoid weight drift in case the system is exposed to external disturbances. The closed-loop system stability is also analyzed using Lyapunov stability theory. Simulations and experimental results are included to validate the effectiveness of the proposed control scheme.

Stable modeling based control methods using a new RBF network.

PubMed

Beyhan, Selami; Alci, Musa

2010-10-01

This paper presents a novel model with radial basis functions (RBFs), which is applied successively for online stable identification and control of nonlinear discrete-time systems. First, the proposed model is utilized for direct inverse modeling of the plant to generate the control input where it is assumed that inverse plant dynamics exist. Second, it is employed for system identification to generate a sliding-mode control input. Finally, the network is employed to tune PID (proportional + integrative + derivative) controller parameters automatically. The adaptive learning rate (ALR), which is employed in the gradient descent (GD) method, provides the global convergence of the modeling errors. Using the Lyapunov stability approach, the boundedness of the tracking errors and the system parameters are shown both theoretically and in real time. To show the superiority of the new model with RBFs, its tracking results are compared with the results of a conventional sigmoidal multi-layer perceptron (MLP) neural network and the new model with sigmoid activation functions. To see the real-time capability of the new model, the proposed network is employed for online identification and control of a cascaded parallel two-tank liquid-level system. Even though there exist large disturbances, the proposed model with RBFs generates a suitable control input to track the reference signal better than other methods in both simulations and real time. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

A new laboratory radio frequency identification (RFID) system for behavioural tracking of marine organisms.

PubMed

Aguzzi, Jacopo; Sbragaglia, Valerio; Sarriá, David; García, José Antonio; Costa, Corrado; del Río, Joaquín; Mànuel, Antoni; Menesatti, Paolo; Sardà, Francesc

2011-01-01

Radio frequency identification (RFID) devices are currently used to quantify several traits of animal behaviour with potential applications for the study of marine organisms. To date, behavioural studies with marine organisms are rare because of the technical difficulty of propagating radio waves within the saltwater medium. We present a novel RFID tracking system to study the burrowing behaviour of a valuable fishery resource, the Norway lobster (Nephrops norvegicus L.). The system consists of a network of six controllers, each handling a group of seven antennas. That network was placed below a microcosm tank that recreated important features typical of Nephrops' grounds, such as the presence of multiple burrows. The animals carried a passive transponder attached to their telson, operating at 13.56 MHz. The tracking system was implemented to concurrently report the behaviour of up to three individuals, in terms of their travelled distances in a specified unit of time and their preferential positioning within the antenna network. To do so, the controllers worked in parallel to send the antenna data to a computer via a USB connection. The tracking accuracy of the system was evaluated by concurrently recording the animals' behaviour with automated video imaging. During the two experiments, each lasting approximately one week, two different groups of three animals each showed a variable burrow occupancy and a nocturnal displacement under a standard photoperiod regime (12 h light:12 h dark), measured using the RFID method. Similar results were obtained with the video imaging. Our implemented RFID system was therefore capable of efficiently tracking the tested organisms and has a good potential for use on a wide variety of other marine organisms of commercial, aquaculture, and ecological interest.

A New Laboratory Radio Frequency Identification (RFID) System for Behavioural Tracking of Marine Organisms

PubMed Central

Aguzzi, Jacopo; Sbragaglia, Valerio; Sarriá, David; García, José Antonio; Costa, Corrado; del Río, Joaquín; Mànuel, Antoni; Menesatti, Paolo; Sardà, Francesc

2011-01-01

Radio frequency identification (RFID) devices are currently used to quantify several traits of animal behaviour with potential applications for the study of marine organisms. To date, behavioural studies with marine organisms are rare because of the technical difficulty of propagating radio waves within the saltwater medium. We present a novel RFID tracking system to study the burrowing behaviour of a valuable fishery resource, the Norway lobster (Nephrops norvegicus L.). The system consists of a network of six controllers, each handling a group of seven antennas. That network was placed below a microcosm tank that recreated important features typical of Nephrops’ grounds, such as the presence of multiple burrows. The animals carried a passive transponder attached to their telson, operating at 13.56 MHz. The tracking system was implemented to concurrently report the behaviour of up to three individuals, in terms of their travelled distances in a specified unit of time and their preferential positioning within the antenna network. To do so, the controllers worked in parallel to send the antenna data to a computer via a USB connection. The tracking accuracy of the system was evaluated by concurrently recording the animals’ behaviour with automated video imaging. During the two experiments, each lasting approximately one week, two different groups of three animals each showed a variable burrow occupancy and a nocturnal displacement under a standard photoperiod regime (12 h light:12 h dark), measured using the RFID method. Similar results were obtained with the video imaging. Our implemented RFID system was therefore capable of efficiently tracking the tested organisms and has a good potential for use on a wide variety of other marine organisms of commercial, aquaculture, and ecological interest. PMID:22163710

The telecommunications and data acquisition report

NASA Technical Reports Server (NTRS)

Renzetti, N. A.

1980-01-01

Deep Space Network progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implemention, and operations is documented. In addition, developments in Earth based radio technology as applied to geodynamics, astrophysics, and the radio search for extraterrestrial intelligence are reported.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1986-01-01

Deep Space Network progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is documented. In addition, developments in Earth-based radio technology as applied to geodynamics, astrophysics and the radio search for extraterrestrial intelligence are reported.

Wide-Area Cooperative Biometric Tagging, Tracking and Locating in a Multimodal Sensor Network

DTIC Science & Technology

2014-12-04

12] 89.3% 2.7% 7 5 50s Our Model 90.7% 2.7% 6 5 4.6s TABLE III: Comparison of tracking results on CAVIAR dataset. The number of trajectories in...other. We evaluate our approach on two widely used public single-camera pedestrian tracking datasets: the CAVIAR dataset [1] and the TownCentre dataset...collaborators at Progeny. It is also being provided to ONR along with datasets on which it has been tested. REFERENCES [1] Caviar dataset. http

Visibility graphlet approach to chaotic time series

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mutua, Stephen; Computer Science Department, Masinde Muliro University of Science and Technology, P.O. Box 190-50100, Kakamega; Gu, Changgui, E-mail: gu-changgui@163.com, E-mail: hjyang@ustc.edu.cn

Many novel methods have been proposed for mapping time series into complex networks. Although some dynamical behaviors can be effectively captured by existing approaches, the preservation and tracking of the temporal behaviors of a chaotic system remains an open problem. In this work, we extended the visibility graphlet approach to investigate both discrete and continuous chaotic time series. We applied visibility graphlets to capture the reconstructed local states, so that each is treated as a node and tracked downstream to create a temporal chain link. Our empirical findings show that the approach accurately captures the dynamical properties of chaotic systems.more » Networks constructed from periodic dynamic phases all converge to regular networks and to unique network structures for each model in the chaotic zones. Furthermore, our results show that the characterization of chaotic and non-chaotic zones in the Lorenz system corresponds to the maximal Lyapunov exponent, thus providing a simple and straightforward way to analyze chaotic systems.« less

Containment control of networked autonomous underwater vehicles: A predictor-based neural DSC design.

PubMed

Peng, Zhouhua; Wang, Dan; Wang, Wei; Liu, Lu

2015-11-01

This paper investigates the containment control problem of networked autonomous underwater vehicles in the presence of model uncertainty and unknown ocean disturbances. A predictor-based neural dynamic surface control design method is presented to develop the distributed adaptive containment controllers, under which the trajectories of follower vehicles nearly converge to the dynamic convex hull spanned by multiple reference trajectories over a directed network. Prediction errors, rather than tracking errors, are used to update the neural adaptation laws, which are independent of the tracking error dynamics, resulting in two time-scales to govern the entire system. The stability property of the closed-loop network is established via Lyapunov analysis, and transient property is quantified in terms of L2 norms of the derivatives of neural weights, which are shown to be smaller than the classical neural dynamic surface control approach. Comparative studies are given to show the substantial improvements of the proposed new method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Constructing a generalized network design model to study air distribution in ventilation networks in subway with a single-track tunnel

NASA Astrophysics Data System (ADS)

Lugin, IV

2018-03-01

In focus are the features of construction of the generalized design model for the network method to study air distribution in ventilation system in subway with the single-track tunnel. The generalizations, assumptions and simplifications included in the model are specified. The air distribution is calculated with regard to the influence of topology and air resistances of the ventilation network sections. The author studies two variants of the subway line: half-open and closed with dead end on the both sides. It is found that the total air exchange at a subway station depends on the station location within the line. The operating mode of fans remains unaltered in this case. The article shows that elimination of air leakage in the station ventilation room allows an increase in the air flow rate by 7–8% at the same energy consumption by fans. The influence of the stop of a train in the tunnel on the air distribution is illustrated.

Adaptation of a software development methodology to the implementation of a large-scale data acquisition and control system. [for Deep Space Network

NASA Technical Reports Server (NTRS)

Madrid, G. A.; Westmoreland, P. T.

1983-01-01

A progress report is presented on a program to upgrade the existing NASA Deep Space Network in terms of a redesigned computer-controlled data acquisition system for channelling tracking, telemetry, and command data between a California-based control center and three signal processing centers in Australia, California, and Spain. The methodology for the improvements is oriented towards single subsystem development with consideration for a multi-system and multi-subsystem network of operational software. Details of the existing hardware configurations and data transmission links are provided. The program methodology includes data flow design, interface design and coordination, incremental capability availability, increased inter-subsystem developmental synthesis and testing, system and network level synthesis and testing, and system verification and validation. The software has been implemented thus far to a 65 percent completion level, and the methodology being used to effect the changes, which will permit enhanced tracking and communication with spacecraft, has been concluded to feature effective techniques.

Dual adaptive dynamic control of mobile robots using neural networks.

PubMed

Bugeja, Marvin K; Fabri, Simon G; Camilleri, Liberato

2009-02-01

This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.

MEMS-based beam-steerable free-space optical communication link for reconfigurable wireless data center

NASA Astrophysics Data System (ADS)

Deng, Peng; Kavehrad, Mohsen; Lou, Yan

2017-01-01

Flexible wireless datacenter networks based on free space optical communication (FSO) links are being considered as promising solutions to meet the future datacenter demands of high throughput, robustness to dynamic traffic patterns, cabling complexity and energy efficiency. Robust and precise steerable FSO links over dynamic traffic play a key role in the reconfigurable optical wireless datacenter inter-rack network. In this work, we propose and demonstrate a reconfigurable 10Gbps FSO system incorporated with smart beam acquisition and tracking mechanism based on gimballess two-axis MEMS micro-mirror and retro-reflective film marked aperture. The fast MEMS-based beam acquisition switches laser beam of FSO terminal from one rack to the next for reconfigurable networks, and the precise beam tracking makes FSO device auto-correct the misalignment in real-time. We evaluate the optical power loss and bit error rate performance of steerable FSO links at various directions. Experimental results suggest that the MEMS based beam steerable FSO links hold considerable promise for the future reconfigurable wireless datacenter networks.

Deep Space Network-Wide Portal Development: Planning Service Pilot Project

NASA Technical Reports Server (NTRS)

Doneva, Silviya

2011-01-01

The Deep Space Network (DSN) is an international network of antennas that supports interplanetary spacecraft missions and radio and radar astronomy observations for the exploration of the solar system and the universe. DSN provides the vital two-way communications link that guides and controls planetary explorers, and brings back the images and new scientific information they collect. In an attempt to streamline operations and improve overall services provided by the Deep Space Network a DSN-wide portal is under development. The project is one step in a larger effort to centralize the data collected from current missions including user input parameters for spacecraft to be tracked. This information will be placed into a principal repository where all operations related to the DSN are stored. Furthermore, providing statistical characterization of data volumes will help identify technically feasible tracking opportunities and more precise mission planning by providing upfront scheduling proposals. Business intelligence tools are to be incorporated in the output to deliver data visualization.

Specific contributions of basal ganglia and cerebellum to the neural tracking of rhythm.

PubMed

Nozaradan, Sylvie; Schwartze, Michael; Obermeier, Christian; Kotz, Sonja A

2017-10-01

How specific brain networks track rhythmic sensory input over time remains a challenge in neuroimaging work. Here we show that subcortical areas, namely the basal ganglia and the cerebellum, specifically contribute to the neural tracking of rhythm. We tested patients with focal lesions in either of these areas and healthy controls by means of electroencephalography (EEG) while they listened to rhythmic sequences known to induce selective neural tracking at a frequency corresponding to the most-often perceived pulse-like beat. Both patients and controls displayed neural responses to the rhythmic sequences. However, these response patterns were different across groups, with patients showing reduced tracking at beat frequency, especially for the more challenging rhythms. In the cerebellar patients, this effect was specific to the rhythm played at a fast tempo, which places high demands on the temporally precise encoding of events. In contrast, basal ganglia patients showed more heterogeneous responses at beat frequency specifically for the most complex rhythm, which requires more internal generation of the beat. These findings provide electrophysiological evidence that these subcortical structures selectively shape the neural representation of rhythm. Moreover, they suggest that the processing of rhythmic auditory input relies on an extended cortico-subcortico-cortical functional network providing specific timing and entrainment sensitivities. Copyright © 2017 Elsevier Ltd. All rights reserved.

Using Wireless Sensor Networks and Trains as Data Mules to Monitor Slab Track Infrastructures.

PubMed

Cañete, Eduardo; Chen, Jaime; Díaz, Manuel; Llopis, Luis; Reyna, Ana; Rubio, Bartolomé

2015-06-26

Recently, slab track systems have arisen as a safer and more sustainable option for high speed railway infrastructures, compared to traditional ballasted tracks. Integrating Wireless Sensor Networks within these infrastructures can provide structural health related data that can be used to evaluate their degradation and to not only detect failures but also to predict them. The design of such systems has to deal with a scenario of large areas with inaccessible zones, where neither Internet coverage nor electricity supply is guaranteed. In this paper we propose a monitoring system for slab track systems that measures vibrations and displacements in the track. Collected data is transmitted to passing trains, which are used as data mules to upload the information to a remote control center. On arrival at the station, the data is stored in a database, which is queried by an application in order to detect and predict failures. In this paper, different communication architectures are designed and tested to select the most suitable system meeting such requirements as efficiency, low cost and data accuracy. In addition, to ensure communication between the sensing devices and the train, the communication system must take into account parameters such as train speed, antenna coverage, band and frequency.

Using Wireless Sensor Networks and Trains as Data Mules to Monitor Slab Track Infrastructures

PubMed Central

Cañete, Eduardo; Chen, Jaime; Díaz, Manuel; Llopis, Luis; Reyna, Ana; Rubio, Bartolomé

2015-01-01

Recently, slab track systems have arisen as a safer and more sustainable option for high speed railway infrastructures, compared to traditional ballasted tracks. Integrating Wireless Sensor Networks within these infrastructures can provide structural health related data that can be used to evaluate their degradation and to not only detect failures but also to predict them. The design of such systems has to deal with a scenario of large areas with inaccessible zones, where neither Internet coverage nor electricity supply is guaranteed. In this paper we propose a monitoring system for slab track systems that measures vibrations and displacements in the track. Collected data is transmitted to passing trains, which are used as data mules to upload the information to a remote control center. On arrival at the station, the data is stored in a database, which is queried by an application in order to detect and predict failures. In this paper, different communication architectures are designed and tested to select the most suitable system meeting such requirements as efficiency, low cost and data accuracy. In addition, to ensure communication between the sensing devices and the train, the communication system must take into account parameters such as train speed, antenna coverage, band and frequency. PMID:26131668

Research of PV Power Generation MPPT based on GABP Neural Network

NASA Astrophysics Data System (ADS)

Su, Yu; Lin, Xianfu

2018-05-01

Photovoltaic power generation has become the main research direction of new energy power generation. But high investment and low efficiency of photovoltaic industry arouse concern in some extent. So maximum power point tracking of photovoltaic power generation has been a popular study point. Due to slow response, oscillation at maximum power point and low precision, the algorithm based on genetic algorithm combined with BP neural network are designed detailedly in this paper. And the modeling and simulation are completed by use of MATLAB/SIMULINK. The results show that the algorithm is effective and the maximum power point can be tracked accurately and quickly.

Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking

PubMed Central

Swan, Melanie

2009-01-01

A new class of patient-driven health care services is emerging to supplement and extend traditional health care delivery models and empower patient self-care. Patient-driven health care can be characterized as having an increased level of information flow, transparency, customization, collaboration and patient choice and responsibility-taking, as well as quantitative, predictive and preventive aspects. The potential exists to both improve traditional health care systems and expand the concept of health care though new services. This paper examines three categories of novel health services: health social networks, consumer personalized medicine and quantified self-tracking. PMID:19440396

Zebrafish tracking using convolutional neural networks.

PubMed

Xu, Zhiping; Cheng, Xi En

2017-02-17

Keeping identity for a long term after occlusion is still an open problem in the video tracking of zebrafish-like model animals, and accurate animal trajectories are the foundation of behaviour analysis. We utilize the highly accurate object recognition capability of a convolutional neural network (CNN) to distinguish fish of the same congener, even though these animals are indistinguishable to the human eye. We used data augmentation and an iterative CNN training method to optimize the accuracy for our classification task, achieving surprisingly accurate trajectories of zebrafish of different size and age zebrafish groups over different time spans. This work will make further behaviour analysis more reliable.

Zebrafish tracking using convolutional neural networks

NASA Astrophysics Data System (ADS)

Xu, Zhiping; Cheng, Xi En

2017-02-01

Keeping identity for a long term after occlusion is still an open problem in the video tracking of zebrafish-like model animals, and accurate animal trajectories are the foundation of behaviour analysis. We utilize the highly accurate object recognition capability of a convolutional neural network (CNN) to distinguish fish of the same congener, even though these animals are indistinguishable to the human eye. We used data augmentation and an iterative CNN training method to optimize the accuracy for our classification task, achieving surprisingly accurate trajectories of zebrafish of different size and age zebrafish groups over different time spans. This work will make further behaviour analysis more reliable.

Neural mechanisms tracking popularity in real-world social networks.

PubMed

Zerubavel, Noam; Bearman, Peter S; Weber, Jochen; Ochsner, Kevin N

2015-12-08

Differences in popularity are a key aspect of status in virtually all human groups and shape social interactions within them. Little is known, however, about how we track and neurally represent others' popularity. We addressed this question in two real-world social networks using sociometric methods to quantify popularity. Each group member (perceiver) viewed faces of every other group member (target) while whole-brain functional MRI data were collected. Independent functional localizer tasks were used to identify brain systems supporting affective valuation (ventromedial prefrontal cortex, ventral striatum, amygdala) and social cognition (dorsomedial prefrontal cortex, precuneus, temporoparietal junction), respectively. During the face-viewing task, activity in both types of neural systems tracked targets' sociometric popularity, even when controlling for potential confounds. The target popularity-social cognition system relationship was mediated by valuation system activity, suggesting that observing popular individuals elicits value signals that facilitate understanding their mental states. The target popularity-valuation system relationship was strongest for popular perceivers, suggesting enhanced sensitivity to differences among other group members' popularity. Popular group members also demonstrated greater interpersonal sensitivity by more accurately predicting how their own personalities were perceived by other individuals in the social network. These data offer insights into the mechanisms by which status guides social behavior.

Enhancing diversity in the public health research workforce: the research and mentorship program for future HIV vaccine scientists.

PubMed

Sopher, Carrie J; Adamson, Blythe Jane S; Andrasik, Michele P; Flood, Danna M; Wakefield, Steven F; Stoff, David M; Cook, Ryan S; Kublin, James G; Fuchs, Jonathan D

2015-04-01

We developed and evaluated a novel National Institutes of Health-sponsored Research and Mentorship Program for African American and Hispanic medical students embedded within the international, multisite HIV Vaccine Trials Network, and explored its impact on scientific knowledge, acquired skills, and future career plans. Scholars conducted social, behavioral, clinical, or laboratory-based research projects with HIV Vaccine Trials Network investigators over 8 to 16 weeks (track 1) or 9 to 12 months (track 2). We conducted an in-depth, mixed-methods evaluation of the first 2 cohorts (2011-2013) to identify program strengths, areas for improvement, and influence on professional development. A pre-post program assessment demonstrated increases in self-reported knowledge, professional skills, and interest in future HIV vaccine research. During in-depth interviews, scholars reported that a supportive, centrally administered program; available funding; and highly involved mentors and staff were keys to the program's early success. A multicomponent, mentored research experience that engages medical students from underrepresented communities and is organized within a clinical trials network may expand the pool of diverse public health scientists. Efforts to sustain scholar interest over time and track career trajectories are warranted.

Optical neural network system for pose determination of spinning satellites

NASA Technical Reports Server (NTRS)

Lee, Andrew; Casasent, David

1990-01-01

An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.

An improved genetic algorithm for increasing the addressing accuracy of encoding fiber Bragg grating sensor network

NASA Astrophysics Data System (ADS)

Liu, Huanlin; Wang, Chujun; Chen, Yong

2018-01-01

Large-capacity encoding fiber Bragg grating (FBG) sensor network is widely used in modern long-term health monitoring system. Encoding FBG sensors have greatly improved the capacity of distributed FBG sensor network. However, the error of addressing increases correspondingly with the enlarging of capacity. To address the issue, an improved algorithm called genetic tracking algorithm (GTA) is proposed in the paper. In the GTA, for improving the success rate of matching and reducing the large number of redundant matching operations generated by sequential matching, the individuals are designed based on the feasible matching. Then, two kinds of self-crossover ways and a dynamic variation during mutation process are designed to increase the diversity of individuals and to avoid falling into local optimum. Meanwhile, an assistant decision is proposed to handle the issue that the GTA cannot solve when the variation of sensor information is highly overlapped. The simulation results indicate that the proposed GTA has higher accuracy compared with the traditional tracking algorithm and the enhanced tracking algorithm. In order to address the problems of spectrum fragmentation and low sharing degree of spectrum resources in survivable.

Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity.

PubMed

Pedretti, G; Milo, V; Ambrogio, S; Carboni, R; Bianchi, S; Calderoni, A; Ramaswamy, N; Spinelli, A S; Ielmini, D

2017-07-13

Brain-inspired computation can revolutionize information technology by introducing machines capable of recognizing patterns (images, speech, video) and interacting with the external world in a cognitive, humanlike way. Achieving this goal requires first to gain a detailed understanding of the brain operation, and second to identify a scalable microelectronic technology capable of reproducing some of the inherent functions of the human brain, such as the high synaptic connectivity (~10 4 ) and the peculiar time-dependent synaptic plasticity. Here we demonstrate unsupervised learning and tracking in a spiking neural network with memristive synapses, where synaptic weights are updated via brain-inspired spike timing dependent plasticity (STDP). The synaptic conductance is updated by the local time-dependent superposition of pre- and post-synaptic spikes within a hybrid one-transistor/one-resistor (1T1R) memristive synapse. Only 2 synaptic states, namely the low resistance state (LRS) and the high resistance state (HRS), are sufficient to learn and recognize patterns. Unsupervised learning of a static pattern and tracking of a dynamic pattern of up to 4 × 4 pixels are demonstrated, paving the way for intelligent hardware technology with up-scaled memristive neural networks.

An Optimization-Based State Estimatioin Framework for Large-Scale Natural Gas Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jalving, Jordan; Zavala, Victor M.

We propose an optimization-based state estimation framework to track internal spacetime flow and pressure profiles of natural gas networks during dynamic transients. We find that the estimation problem is ill-posed (because of the infinite-dimensional nature of the states) and that this leads to instability of the estimator when short estimation horizons are used. To circumvent this issue, we propose moving horizon strategies that incorporate prior information. In particular, we propose a strategy that initializes the prior using steady-state information and compare its performance against a strategy that does not initialize the prior. We find that both strategies are capable ofmore » tracking the state profiles but we also find that superior performance is obtained with steady-state prior initialization. We also find that, under the proposed framework, pressure sensor information at junctions is sufficient to track the state profiles. We also derive approximate transport models and show that some of these can be used to achieve significant computational speed-ups without sacrificing estimation performance. We show that the estimator can be easily implemented in the graph-based modeling framework Plasmo.jl and use a multipipeline network study to demonstrate the developments.« less

Applying a Space-Based Security Recovery Scheme for Critical Homeland Security Cyberinfrastructure Utilizing the NASA Tracking and Data Relay (TDRS) Based Space Network

NASA Technical Reports Server (NTRS)

Shaw, Harry C.; McLaughlin, Brian; Stocklin, Frank; Fortin, Andre; Israel, David; Dissanayake, Asoka; Gilliand, Denise; LaFontaine, Richard; Broomandan, Richard; Hyunh, Nancy

2015-01-01

Protection of the national infrastructure is a high priority for cybersecurity of the homeland. Critical infrastructure such as the national power grid, commercial financial networks, and communications networks have been successfully invaded and re-invaded from foreign and domestic attackers. The ability to re-establish authentication and confidentiality of the network participants via secure channels that have not been compromised would be an important countermeasure to compromise of our critical network infrastructure. This paper describes a concept of operations by which the NASA Tracking and Data Relay (TDRS) constellation of spacecraft in conjunction with the White Sands Complex (WSC) Ground Station host a security recovery system for re-establishing secure network communications in the event of a national or regional cyberattack. Users would perform security and network restoral functions via a Broadcast Satellite Service (BSS) from the TDRS constellation. The BSS enrollment only requires that each network location have a receive antenna and satellite receiver. This would be no more complex than setting up a DIRECTTV-like receiver at each network location with separate network connectivity. A GEO BSS would allow a mass re-enrollment of network nodes (up to nationwide) simultaneously depending upon downlink characteristics. This paper details the spectrum requirements, link budget, notional assets and communications requirements for the scheme. It describes the architecture of such a system and the manner in which it leverages off of the existing secure infrastructure which is already in place and managed by the NASAGSFC Space Network Project.

Department of the Navy Naval Networking Environment (NNE)-2016. Strategic Definition, Scope and Strategy Paper, Version 1.1

DTIC Science & Technology

2008-05-13

IA capabilities applied to protect, defend, and respond to them. This will provide decision makers and network operators, at all command levels...procedures to recognize, react, and respond to potential system and network compromises must be in place and provide control sufficient to protect the...to respond to and track users’ needs. • Information Service Visibility. Interview responses described a need for the reporting of network status and

Surface adsorption and hopping cause probe-size-dependent microrheology of actin networks

NASA Astrophysics Data System (ADS)

He, Jun; Tang, Jay X.

2011-04-01

A network of filaments formed primarily by the abundant cytoskeletal protein actin gives animal cells their shape and elasticity. The rheological properties of reconstituted actin networks have been studied by tracking micron-sized probe beads embedded within the networks. We investigate how microrheology depends on surface properties of probe particles by varying the stickiness of their surface. For this purpose, we chose carboxylate polystyrene (PS) beads, silica beads, bovine serum albumin (BSA) -coated PS beads, and polyethylene glycol (PEG) -grafted PS beads, which show descending stickiness to actin filaments, characterized by confocal imaging and microrheology. Probe size dependence of microrheology is observed for all four types of beads. For the slippery PEG beads, particle-tracking microrheology detects weaker networks using smaller beads, which tend to diffuse through the network by hopping from one confinement “cage” to another. This trend is reversed for the other three types of beads, for which microrheology measures stiffer networks for smaller beads due to physisorption of nearby filaments to the bead surface. We explain the probe size dependence with two simple models. We also evaluate depletion effect near nonadsorption bead surface using quantitative image analysis and discuss the possible impact of depletion on microrheology. Analysis of these effects is necessary in order to accurately define the actin network rheology both in vitro and in vivo.

Electrical localization of weakly electric fish using neural networks

NASA Astrophysics Data System (ADS)

Kiar, Greg; Mamatjan, Yasin; Jun, James; Maler, Len; Adler, Andy

2013-04-01

Weakly Electric Fish (WEF) emit an Electric Organ Discharge (EOD), which travels through the surrounding water and enables WEF to locate nearby objects or to communicate between individuals. Previous tracking of WEF has been conducted using infrared (IR) cameras and subsequent image processing. The limitation of visual tracking is its relatively low frame-rate and lack of reliability when visually obstructed. Thus, there is a need for reliable monitoring of WEF location and behaviour. The objective of this study is to provide an alternative and non-invasive means of tracking WEF in real-time using neural networks (NN). This study was carried out in three stages. First stage was to recreate voltage distributions by simulating the WEF using EIDORS and finite element method (FEM) modelling. Second stage was to validate the model using phantom data acquired from an Electrical Impedance Tomography (EIT) based system, including a phantom fish and tank. In the third stage, the measurement data was acquired using a restrained WEF within a tank. We trained the NN based on the voltage distributions for different locations of the WEF. With networks trained on the acquired data, we tracked new locations of the WEF and observed the movement patterns. The results showed a strong correlation between expected and calculated values of WEF position in one dimension, yielding a high spatial resolution within 1 cm and 10 times higher temporal resolution than IR cameras. Thus, the developed approach could be used as a practical method to non-invasively monitor the WEF in real-time.

Water resources: Research network to track alpine water

USDA-ARS?s Scientific Manuscript database

The water cycle in alpine environments worldwide supplies fresh water to vast downstream areas inhabited by more than half of humanity. The International Network for Alpine Research Catchment Hydrology (INARCH) was launched this year by the Global Energy and Water Exchanges project of the World Clim...

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1985-01-01

Deep Space Network (DSN) progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operation is discussed. In addition, developments in Earth-based radio technology as applied to geodynamics, astrophysics and the radio search for extraterrestrial intelligence are reported.

Managing Information Technology as a Catalyst of Change. Track V: Optimizing the Infrastructure.

ERIC Educational Resources Information Center

CAUSE, Boulder, CO.

This track of the 1993 CAUSE Conference presents eight papers on developments in computer network infrastructure and the challenges for those who plan for, implement, and manage it in colleges and universities. Papers include: (1) "Where Do We Go from Here: Summative Assessment of a Five-Year Strategic Plan for Linking and Integrating…

Arc tracking of cables for space applications

NASA Technical Reports Server (NTRS)

Koenig, D.; Frontzek, F. R.; Hanson, J.; Reher, H. J.; Judd, M. D.; Bryant, D.

1995-01-01

The main objective of this study is to develop a new test method that is suitable for the assessment of the resistance of aerospace cables to arc tracking for different specific environmental and network conditions of spacecrafts. This paper reports the purpose, test conditions, test specimen, test procedure, and test acceptance criteria of seven different (200-250 mm long) cables.

Simulation and Modeling of a Novel Medium Access Control Scheme for Multi-Beam Directional Networking

DTIC Science & Technology

2017-03-03

When a neighbor receives one of these packets, it waits until the end of the transmit time and then responds with its own hello packet, containing its...and 3 respond with their own hello packet. Location Tracking Another important feature is location tracking. Due to node mobility, it is vital that

Observing Ocean Ecosystems with Sonar

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matzner, Shari; Maxwell, Adam R.; Ham, Kenneth D.

2016-12-01

We present a real-time processing system for sonar to detect and track animals, and to extract water column biomass statistics in order to facilitate continuous monitoring of an underwater environment. The Nekton Interaction Monitoring System (NIMS) is built to connect to an instrumentation network, where it consumes a real-time stream of sonar data and archives tracking and biomass data.

Multiple object tracking using the shortest path faster association algorithm.

PubMed

Xi, Zhenghao; Liu, Heping; Liu, Huaping; Yang, Bin

2014-01-01

To solve the persistently multiple object tracking in cluttered environments, this paper presents a novel tracking association approach based on the shortest path faster algorithm. First, the multiple object tracking is formulated as an integer programming problem of the flow network. Then we relax the integer programming to a standard linear programming problem. Therefore, the global optimum can be quickly obtained using the shortest path faster algorithm. The proposed method avoids the difficulties of integer programming, and it has a lower worst-case complexity than competing methods but better robustness and tracking accuracy in complex environments. Simulation results show that the proposed algorithm takes less time than other state-of-the-art methods and can operate in real time.

Multiple Object Tracking Using the Shortest Path Faster Association Algorithm

PubMed Central

Liu, Heping; Liu, Huaping; Yang, Bin

2014-01-01

To solve the persistently multiple object tracking in cluttered environments, this paper presents a novel tracking association approach based on the shortest path faster algorithm. First, the multiple object tracking is formulated as an integer programming problem of the flow network. Then we relax the integer programming to a standard linear programming problem. Therefore, the global optimum can be quickly obtained using the shortest path faster algorithm. The proposed method avoids the difficulties of integer programming, and it has a lower worst-case complexity than competing methods but better robustness and tracking accuracy in complex environments. Simulation results show that the proposed algorithm takes less time than other state-of-the-art methods and can operate in real time. PMID:25215322

The Deep Space Network. An instrument for radio navigation of deep space probes

NASA Technical Reports Server (NTRS)

Renzetti, N. A.; Jordan, J. F.; Berman, A. L.; Wackley, J. A.; Yunck, T. P.

1982-01-01

The Deep Space Network (DSN) network configurations used to generate the navigation observables and the basic process of deep space spacecraft navigation, from data generation through flight path determination and correction are described. Special emphasis is placed on the DSN Systems which generate the navigation data: the DSN Tracking and VLBI Systems. In addition, auxiliary navigational support functions are described.

Self organization of wireless sensor networks using ultra-wideband radios

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dowla, Farid U; Nekoogar, Franak; Spiridon, Alex

A novel UWB communications method and system that provides self-organization for wireless sensor networks is introduced. The self-organization is in terms of scalability, power conservation, channel estimation, and node synchronization in wireless sensor networks. The UWB receiver in the present invention adds two new tasks to conventional TR receivers. The two additional units are SNR enhancing unit and timing acquisition and tracking unit.

Delay/Disruption Tolerant Networks for Human Space Flight Video Project

NASA Technical Reports Server (NTRS)

Fink, Patrick W.; Ngo, Phong; Schlesinger, Adam

2010-01-01

The movie describes collaboration between NASA and Vint Cerf on the development of Disruption Tolerant Networks (DTN) for use in space exploration. Current evaluation efforts at Johnson Space Center are focused on the use of DTNs in space communications. Tests include the ability of rovers to store data for later display, tracking local and remote habitat inventory using radio-frequency identification tags, and merging networks.

Improving Department of Defense Global Distribution Performance Through Network Analysis

DTIC Science & Technology

2016-06-01

network performance increase. 14. SUBJECT TERMS supply chain metrics, distribution networks, requisition shipping time, strategic distribution database...peace and war” (p. 4). USTRANSCOM Metrics and Analysis Branch defines, develops, tracks, and maintains outcomes- based supply chain metrics to...2014a, p. 8). The Joint Staff defines a TDD standard as the maximum number of days the supply chain can take to deliver requisitioned materiel

Performance of the all-digital data-transition tracking loop in the advanced receiver

NASA Astrophysics Data System (ADS)

Cheng, U.; Hinedi, S.

1989-11-01

The performance of the all-digital data-transition tracking loop (DTTL) with coherent or noncoherent sampling is described. The effects of few samples per symbol and of noncommensurate sampling rates and symbol rates are addressed and analyzed. Their impacts on the loop phase-error variance and the mean time to lose lock (MTLL) are quantified through computer simulations. The analysis and preliminary simulations indicate that with three to four samples per symbol, the DTTL can track with negligible jitter because of the presence of earth Doppler rate. Furthermore, the MTLL is also expected to be large engough to maintain lock over a Deep Space Network track.

Tracking-by-detection of surgical instruments in minimally invasive surgery via the convolutional neural network deep learning-based method.

PubMed

Zhao, Zijian; Voros, Sandrine; Weng, Ying; Chang, Faliang; Li, Ruijian

2017-12-01

Worldwide propagation of minimally invasive surgeries (MIS) is hindered by their drawback of indirect observation and manipulation, while monitoring of surgical instruments moving in the operated body required by surgeons is a challenging problem. Tracking of surgical instruments by vision-based methods is quite lucrative, due to its flexible implementation via software-based control with no need to modify instruments or surgical workflow. A MIS instrument is conventionally split into a shaft and end-effector portions, while a 2D/3D tracking-by-detection framework is proposed, which performs the shaft tracking followed by the end-effector one. The former portion is described by line features via the RANSAC scheme, while the latter is depicted by special image features based on deep learning through a well-trained convolutional neural network. The method verification in 2D and 3D formulation is performed through the experiments on ex-vivo video sequences, while qualitative validation on in-vivo video sequences is obtained. The proposed method provides robust and accurate tracking, which is confirmed by the experimental results: its 3D performance in ex-vivo video sequences exceeds those of the available state-of -the-art methods. Moreover, the experiments on in-vivo sequences demonstrate that the proposed method can tackle the difficult condition of tracking with unknown camera parameters. Further refinements of the method will refer to the occlusion and multi-instrumental MIS applications.

Operating systems and network protocols for wireless sensor networks.

PubMed

Dutta, Prabal; Dunkels, Adam

2012-01-13

Sensor network protocols exist to satisfy the communication needs of diverse applications, including data collection, event detection, target tracking and control. Network protocols to enable these services are constrained by the extreme resource scarcity of sensor nodes-including energy, computing, communications and storage-which must be carefully managed and multiplexed by the operating system. These challenges have led to new protocols and operating systems that are efficient in their energy consumption, careful in their computational needs and miserly in their memory footprints, all while discovering neighbours, forming networks, delivering data and correcting failures.

Smart border: ad-hoc wireless sensor networks for border surveillance

NASA Astrophysics Data System (ADS)

He, Jun; Fallahi, Mahmoud; Norwood, Robert A.; Peyghambarian, Nasser

2011-06-01

Wireless sensor networks have been proposed as promising candidates to provide automated monitoring, target tracking, and intrusion detection for border surveillance. In this paper, we demonstrate an ad-hoc wireless sensor network system for border surveillance. The network consists of heterogeneously autonomous sensor nodes that distributively cooperate with each other to enable a smart border in remote areas. This paper also presents energy-aware and sleeping algorithms designed to maximize the operating lifetime of the deployed sensor network. Lessons learned in building the network and important findings from field experiments are shared in the paper.

Quantization-Based Adaptive Actor-Critic Tracking Control With Tracking Error Constraints.

PubMed

Fan, Quan-Yong; Yang, Guang-Hong; Ye, Dan

2018-04-01

In this paper, the problem of adaptive actor-critic (AC) tracking control is investigated for a class of continuous-time nonlinear systems with unknown nonlinearities and quantized inputs. Different from the existing results based on reinforcement learning, the tracking error constraints are considered and new critic functions are constructed to improve the performance further. To ensure that the tracking errors keep within the predefined time-varying boundaries, a tracking error transformation technique is used to constitute an augmented error system. Specific critic functions, rather than the long-term cost function, are introduced to supervise the tracking performance and tune the weights of the AC neural networks (NNs). A novel adaptive controller with a special structure is designed to reduce the effect of the NN reconstruction errors, input quantization, and disturbances. Based on the Lyapunov stability theory, the boundedness of the closed-loop signals and the desired tracking performance can be guaranteed. Finally, simulations on two connected inverted pendulums are given to illustrate the effectiveness of the proposed method.

The Use of Technology in Participant Tracking and Study Retention: Lessons Learned from a Clinical Trials Network Study

PubMed Central

Mitchell, Shannon Gwin; Schwartz, Robert P.; Alvanzo, Anika A. H.; Weisman, Monique S.; Kyle, Tiffany L.; Turrigiano, Eva M.; Gibson, Martha L.; Perez, Livangelie; McClure, Erin A.; Clingerman, Sara; Froias, Autumn; Shandera, Danielle R.; Walker, Robrina; Babcock, Dean L.; Bailey, Genie L.; Miele, Gloria M.; Kunkel, Lynn E.; Norton, Michael; Stitzer, Maxine L.

2015-01-01

Background The growing use of newer communication and internet technologies, even among low income and transient populations, require research staff to update their outreach strategies to ensure high follow-up and participant retention rates. This paper presents the views of research assistants on the use of cell phones and the internet to track participants in a multi-site randomized trial of substance use disorder treatment. Methods Pre-interview questionnaires exploring tracking and other study-related activities were collected from 21 research staff across the 10 participating US sites. Data were then used to construct a semi-structured interview guide which, in turn, was used to interview 12 of the same staff members. The questionnaires and interview data were entered in Atlas.ti and analyzed for emergent themes related to the use of technology for participant tracking purposes. Results Study staff reported that most participants had cell phones, despite having unstable physical addresses and landlines. The incoming call feature of most cell phones was useful for participants and research staff alike, and texting proved to have additional benefits. However, reliance on participants’ cell phones also proved problematic. Even homeless participants were found to have access to the internet through public libraries and could respond to study staff e-mails. Some study sites opened generic social media accounts, through which study staff sent private messages to participants. However, the Institutional Review Board (IRB) approval process for tracking participants using social media at some sites was prohibitively lengthy. Internet searches through Google, national paid databases, obituaries, and judiciary websites were also helpful tools. Conclusions Research staff perceive that cell phones, internet searches, and social networking sites were effective tools to achieve high follow-up rates in drug abuse research. Studies should incorporate cell phone, texting, and social network website information on locator forms; obtain IRB approval for contacting participants using social networking websites; and include web searches, texting, and the use of social media in staff training as standard operating procedures. PMID:25671593

Visualization and Hierarchical Analysis of Flow in Discrete Fracture Network Models

NASA Astrophysics Data System (ADS)

Aldrich, G. A.; Gable, C. W.; Painter, S. L.; Makedonska, N.; Hamann, B.; Woodring, J.

2013-12-01

Flow and transport in low permeability fractured rock is primary in interconnected fracture networks. Prediction and characterization of flow and transport in fractured rock has important implications in underground repositories for hazardous materials (eg. nuclear and chemical waste), contaminant migration and remediation, groundwater resource management, and hydrocarbon extraction. We have developed methods to explicitly model flow in discrete fracture networks and track flow paths using passive particle tracking algorithms. Visualization and analysis of particle trajectory through the fracture network is important to understanding fracture connectivity, flow patterns, potential contaminant pathways and fast paths through the network. However, occlusion due to the large number of highly tessellated and intersecting fracture polygons preclude the effective use of traditional visualization methods. We would also like quantitative analysis methods to characterize the trajectory of a large number of particle paths. We have solved these problems by defining a hierarchal flow network representing the topology of particle flow through the fracture network. This approach allows us to analyses the flow and the dynamics of the system as a whole. We are able to easily query the flow network, and use paint-and-link style framework to filter the fracture geometry and particle traces based on the flow analytics. This allows us to greatly reduce occlusion while emphasizing salient features such as the principal transport pathways. Examples are shown that demonstrate the methodology and highlight how use of this new method allows quantitative analysis and characterization of flow and transport in a number of representative fracture networks.

ECUANET--European Corporate Academies Transnational Best Practice Network

ERIC Educational Resources Information Center

Dealtry, Richard

2004-01-01

This article describes the formation and forward planning of an important and independent transnational best practice networking development project, i.e. ECUANET. ECUANET's aims are to track and disseminate the evolutions and revolutions that are taking place in the creative management of corporate universities and small and medium enterprise…

Mark 4A DSN receiver-exciter and transmitter subsystems

NASA Technical Reports Server (NTRS)

Wick, M. R.

1986-01-01

The present configuration of the Mark 4A DSN Receiver-Exciter and Transmitter Subsystems is described. Functional requirements and key characteristics are given to show the differences in the capabilities required by the Networks Consolidation task for combined High Earth Orbiter and Deep Space Network tracking support.

Description of cost data from state self-evaluations of commercial vehicle information systems and networks (CVISN) deployments (October 2003 to October 2005)

DOT National Transportation Integrated Search

2006-02-20

To help states track their own progress in deploying Commercial Vehicle Information Systems and Networks (CVISN) technologies, a self-evaluation requirement was included in the partnership agreements between the U.S. Department of Transportation (DOT...

Why Do Academics Use Academic Social Networking Sites?

ERIC Educational Resources Information Center

Meishar-Tal, Hagit; Pieterse, Efrat

2017-01-01

Academic social-networking sites (ASNS) such as Academia.edu and ResearchGate are becoming very popular among academics. These sites allow uploading academic articles, abstracts, and links to published articles; track demand for published articles, and engage in professional interaction. This study investigates the nature of the use and the…

Factors contributing to the hydrologic effectiveness of a rain garden network (Cincinnati OH USA)

EPA Science Inventory

Infiltrative rain gardens add retention capacity to sewersheds, yet, their capacity for detention and redistribution of stormwater runoff is dynamic and often unverified by monitoring. Over a 4-year period, we tracked whole system water fluxes in a two-tier rain garden network, a...

Clock Synchronization for Multihop Wireless Sensor Networks

ERIC Educational Resources Information Center

Solis Robles, Roberto

2009-01-01

In wireless sensor networks, more so generally than in other types of distributed systems, clock synchronization is crucial since by having this service available, several applications such as media access protocols, object tracking, or data fusion, would improve their performance. In this dissertation, we propose a set of algorithms to achieve…

Multitarget tracking in cluttered environment for a multistatic passive radar system under the DAB/DVB network

NASA Astrophysics Data System (ADS)

Shi, Yi Fang; Park, Seung Hyo; Song, Taek Lyul

2017-12-01

The target tracking using multistatic passive radar in a digital audio/video broadcast (DAB/DVB) network with illuminators of opportunity faces two main challenges: the first challenge is that one has to solve the measurement-to-illuminator association ambiguity in addition to the conventional association ambiguity between the measurements and targets, which introduces a significantly complex three-dimensional (3-D) data association problem among the target-measurement illuminator, this is because all the illuminators transmit the same carrier frequency signals and signals transmitted by different illuminators but reflected via the same target become indistinguishable; the other challenge is that only the bistatic range and range-rate measurements are available while the angle information is unavailable or of very poor quality. In this paper, the authors propose a new target tracking algorithm directly in three-dimensional (3-D) Cartesian coordinates with the capability of track management using the probability of target existence as a track quality measure. The proposed algorithm is termed sequential processing-joint integrated probabilistic data association (SP-JIPDA), which applies the modified sequential processing technique to resolve the additional association ambiguity between measurements and illuminators. The SP-JIPDA algorithm sequentially operates the JIPDA tracker to update each track for each illuminator with all the measurements in the common measurement set at each time. For reasons of fair comparison, the existing modified joint probabilistic data association (MJPDA) algorithm that addresses the 3-D data association problem via "supertargets" using gate grouping and provides tracks directly in 3-D Cartesian coordinates, is enhanced by incorporating the probability of target existence as an effective track quality measure for track management. Both algorithms deal with nonlinear observations using the extended Kalman filtering. A simulation study is performed to verify the superiority of the proposed SP-JIPDA algorithm over the MJIPDA in this multistatic passive radar system.

Joint JSC/GSFC two-TDRS navigation certification results for STS-29, STS-30, and STS-32

NASA Technical Reports Server (NTRS)

Schmidt, Thomas G.; Brown, Edward T.; Murdock, Valerie E.; Cappellari, James O., Jr.; Smith, Evan A.; Schmitt, Mark W.; Omalley, James W.; Lowes, Flora B.; Joyce, James B.

1990-01-01

The procedures used and the results obtained in the joint Johnson Space Center (JSC)/Goddard Space Flight Center (GSFC) navigation certification of the two-Tracking and Data Relay Satellite (TDRS) S-band tracking configuration for support of low- to medium-inclination (28.5 to 62 degrees) Shuttle missions (STS-29 and STS-30) and Shuttle rendezvous missions (STS-32) are described. The objective of this certification effort was to certify the two-TDRS configuration for nominal Space Transportation System (STS) on-orbit navigation support, thereby making it possible to significantly reduce the ground tracking support requirements for routine STS on-orbit navigation. JSC had the primary responsibility for certification of the two-TDRS configuration for STS support, and GSFC supported the effort by performing Ground Network (GN) and Space Network (SN) tracking data evaluation, parallel orbit solutions, and solution comparisons. In the certification process, two types of orbit determination solutions were generated by JSC and by GSFC for each tracking arc evaluated, one type using TDRS-East and TDRS-West tracking data combined with ground tracking data (the reference solutions) and one type using only TDRS-East and TDRS-West tracking data. The two types of solutions were then compared to determine the maximum position differences over the solution arcs and whether these differences satisfied the navigation certification criteria. The certification criteria were a function of the type of Shuttle activity in the tracking arc, i.e., quiet, moderate, or active. Quiet periods included no attitude maneuvers or ventings; moderate periods included one or two maneuvers or ventings; and active periods included more than two maneuvers or ventings. The results of the individual JSC and GSFC certification analyses for the STS-29, STS-30, and STS-32 missions and the joint JSC/GSFC conclusions regarding certification of the two-TDRS S-band configuration for STS support are presented.

Economic and Social Factors in Designing Disease Control Strategies for Epidemics on Networks

NASA Astrophysics Data System (ADS)

Kleczkowski, A.; Dybiec, B.; Gilligan, C. A.

2006-11-01

Models for control of epidemics on local, global and small-world networks are considered, with only partial information accessible about the status of individuals and their connections. The main goal of an effective control measure is to stop the epidemic at a lowest possible cost, including treatment and cost necessary to track the disease spread. We show that delay in detection of infectious individuals and presence of long-range links are the most important factors determining the cost. However, the details of long-range links are usually the least-known element of the social interactions due to their occasional character and potentially short life-span. We show that under some conditions on the probability of disease spread, it is advisable to attempt to track those links, even if this involves additional costs. Thus, collecting some additional knowledge about the network structure might be beneficial to ensure a successful and cost-effective control.

Distributed Common Ground System-Navy Increment 2 (DCGS-N Inc 2)

DTIC Science & Technology

2016-03-01

15 minutes Enter and be Managed in the Network: Reference SvcV-7, Consolidated Afloat Networks and Enterprise Services ( CANES ) CDD, DCGS-N Inc 2...Red, White , Gray Data and Tracks to Command and Control System. Continuous Stream from SCI Common Intelligence Picture to General Service (GENSER...AIS - Automatic Information System AOC - Air Operations Command CANES - Consolidated Afloat Networks and Enterprise Services CID - Center for

Sensitivity of marine protected area network connectivity to atmospheric variability

NASA Astrophysics Data System (ADS)

Fox, Alan D.; Henry, Lea-Anne; Corne, David W.; Roberts, J. Murray

2016-11-01

International efforts are underway to establish well-connected systems of marine protected areas (MPAs) covering at least 10% of the ocean by 2020. But the nature and dynamics of ocean ecosystem connectivity are poorly understood, with unresolved effects of climate variability. We used 40-year runs of a particle tracking model to examine the sensitivity of an MPA network for habitat-forming cold-water corals in the northeast Atlantic to changes in larval dispersal driven by atmospheric cycles and larval behaviour. Trajectories of Lophelia pertusa larvae were strongly correlated to the North Atlantic Oscillation (NAO), the dominant pattern of interannual atmospheric circulation variability over the northeast Atlantic. Variability in trajectories significantly altered network connectivity and source-sink dynamics, with positive phase NAO conditions producing a well-connected but asymmetrical network connected from west to east. Negative phase NAO produced reduced connectivity, but notably some larvae tracked westward-flowing currents towards coral populations on the mid-Atlantic ridge. Graph theoretical metrics demonstrate critical roles played by seamounts and offshore banks in larval supply and maintaining connectivity across the network. Larval longevity and behaviour mediated dispersal and connectivity, with shorter lived and passive larvae associated with reduced connectivity. We conclude that the existing MPA network is vulnerable to atmospheric-driven changes in ocean circulation.

Rapid Object Detection Systems, Utilising Deep Learning and Unmanned Aerial Systems (uas) for Civil Engineering Applications

NASA Astrophysics Data System (ADS)

Griffiths, D.; Boehm, J.

2018-05-01

With deep learning approaches now out-performing traditional image processing techniques for image understanding, this paper accesses the potential of rapid generation of Convolutional Neural Networks (CNNs) for applied engineering purposes. Three CNNs are trained on 275 UAS-derived and freely available online images for object detection of 3m2 segments of railway track. These includes two models based on the Faster RCNN object detection algorithm (Resnet and Incpetion-Resnet) as well as the novel onestage Focal Loss network architecture (Retinanet). Model performance was assessed with respect to three accuracy metrics. The first two consisted of Intersection over Union (IoU) with thresholds 0.5 and 0.1. The last assesses accuracy based on the proportion of track covered by object detection proposals against total track length. In under six hours of training (and two hours of manual labelling) the models detected 91.3 %, 83.1 % and 75.6 % of track in the 500 test images acquired from the UAS survey Retinanet, Resnet and Inception-Resnet respectively. We then discuss the potential for such applications of such systems within the engineering field for a range of scenarios.

Tracking control of a closed-chain five-bar robot with two degrees of freedom by integration of an approximation-based approach and mechanical design.

PubMed

Cheng, Long; Hou, Zeng-Guang; Tan, Min; Zhang, W J

2012-10-01

The trajectory tracking problem of a closed-chain five-bar robot is studied in this paper. Based on an error transformation function and the backstepping technique, an approximation-based tracking algorithm is proposed, which can guarantee the control performance of the robotic system in both the stable and transient phases. In particular, the overshoot, settling time, and final tracking error of the robotic system can be all adjusted by properly setting the parameters in the error transformation function. The radial basis function neural network (RBFNN) is used to compensate the complicated nonlinear terms in the closed-loop dynamics of the robotic system. The approximation error of the RBFNN is only required to be bounded, which simplifies the initial "trail-and-error" configuration of the neural network. Illustrative examples are given to verify the theoretical analysis and illustrate the effectiveness of the proposed algorithm. Finally, it is also shown that the proposed approximation-based controller can be simplified by a smart mechanical design of the closed-chain robot, which demonstrates the promise of the integrated design and control philosophy.

Real-time image processing for particle tracking velocimetry

NASA Astrophysics Data System (ADS)

Kreizer, Mark; Ratner, David; Liberzon, Alex

2010-01-01

We present a novel high-speed particle tracking velocimetry (PTV) experimental system. Its novelty is due to the FPGA-based, real-time image processing "on camera". Instead of an image, the camera transfers to the computer using a network card, only the relevant information of the identified flow tracers. Therefore, the system is ideal for the remote particle tracking systems in research and industrial applications, while the camera can be controlled and data can be transferred over any high-bandwidth network. We present the hardware and the open source software aspects of the PTV experiments. The tracking results of the new experimental system has been compared to the flow visualization and particle image velocimetry measurements. The canonical flow in the central cross section of a a cubic cavity (1:1:1 aspect ratio) in our lid-driven cavity apparatus is used for validation purposes. The downstream secondary eddy (DSE) is the sensitive portion of this flow and its size was measured with increasing Reynolds number (via increasing belt velocity). The size of DSE estimated from the flow visualization, PIV and compressed PTV is shown to agree within the experimental uncertainty of the methods applied.

Video redaction: a survey and comparison of enabling technologies

NASA Astrophysics Data System (ADS)

Sah, Shagan; Shringi, Ameya; Ptucha, Raymond; Burry, Aaron; Loce, Robert

2017-09-01

With the prevalence of video recordings from smart phones, dash cams, body cams, and conventional surveillance cameras, privacy protection has become a major concern, especially in light of legislation such as the Freedom of Information Act. Video redaction is used to obfuscate sensitive and personally identifiable information. Today's typical workflow involves simple detection, tracking, and manual intervention. Automated methods rely on accurate detection mechanisms being paired with robust tracking methods across the video sequence to ensure the redaction of all sensitive information while minimizing spurious obfuscations. Recent studies have explored the use of convolution neural networks and recurrent neural networks for object detection and tracking. The present paper reviews the redaction problem and compares a few state-of-the-art detection, tracking, and obfuscation methods as they relate to redaction. The comparison introduces an evaluation metric that is specific to video redaction performance. The metric can be evaluated in a manner that allows balancing the penalty for false negatives and false positives according to the needs of particular application, thereby assisting in the selection of component methods and their associated hyperparameters such that the redacted video has fewer frames that require manual review.

Detection of Spoofed MAC Addresses in 802.11 Wireless Networks

NASA Astrophysics Data System (ADS)

Tao, Kai; Li, Jing; Sampalli, Srinivas

Medium Access Control (MAC) address spoofing is considered as an important first step in a hacker's attempt to launch a variety of attacks on 802.11 wireless networks. Unfortunately, MAC address spoofing is hard to detect. Most current spoofing detection systems mainly use the sequence number (SN) tracking technique, which has drawbacks. Firstly, it may lead to an increase in the number of false positives. Secondly, such techniques cannot be used in systems with wireless cards that do not follow standard 802.11 sequence number patterns. Thirdly, attackers can forge sequence numbers, thereby causing the attacks to go undetected. We present a new architecture called WISE GUARD (Wireless Security Guard) for detection of MAC address spoofing on 802.11 wireless LANs. It integrates three detection techniques - SN tracking, Operating System (OS) fingerprinting & tracking and Received Signal Strength (RSS) fingerprinting & tracking. It also includes the fingerprinting of Access Point (AP) parameters as an extension to the OS fingerprinting for detection of AP address spoofing. We have implemented WISE GUARD on a test bed using off-the-shelf wireless devices and open source drivers. Experimental results show that the new design enhances the detection effectiveness and reduces the number of false positives in comparison with current approaches.

Applying traditional signal processing techniques to social media exploitation for situational understanding

NASA Astrophysics Data System (ADS)

Abdelzaher, Tarek; Roy, Heather; Wang, Shiguang; Giridhar, Prasanna; Al Amin, Md. Tanvir; Bowman, Elizabeth K.; Kolodny, Michael A.

2016-05-01

Signal processing techniques such as filtering, detection, estimation and frequency domain analysis have long been applied to extract information from noisy sensor data. This paper describes the exploitation of these signal processing techniques to extract information from social networks, such as Twitter and Instagram. Specifically, we view social networks as noisy sensors that report events in the physical world. We then present a data processing stack for detection, localization, tracking, and veracity analysis of reported events using social network data. We show using a controlled experiment that the behavior of social sources as information relays varies dramatically depending on context. In benign contexts, there is general agreement on events, whereas in conflict scenarios, a significant amount of collective filtering is introduced by conflicted groups, creating a large data distortion. We describe signal processing techniques that mitigate such distortion, resulting in meaningful approximations of actual ground truth, given noisy reported observations. Finally, we briefly present an implementation of the aforementioned social network data processing stack in a sensor network analysis toolkit, called Apollo. Experiences with Apollo show that our techniques are successful at identifying and tracking credible events in the physical world.

Student chapters: effective dissemination networks for informal optics and photonics education

NASA Astrophysics Data System (ADS)

Fabian, Dirk; Vermeulen, Nathalie; Van Overmeire, Sara

2009-06-01

Professional societies sponsor student chapters in order to foster scholarship and training in photonics at the college and graduate level, but they are also an excellent resource for disseminating photonics knowledge to pre-college students and teachers. Starting in 2006, we tracked the involvement of SPIE student chapter volunteers in informal pre-college education settings. Chapter students reached 2800, 4900 and 11800 pre-college students respectively from 2006-2008 with some form of informal instruction in optics and photonics. As a case study, the EduKit, a self-contained instruction module featuring refractive and diffractive micro-optics developed by the European Network of Excellence on Micro-Optics (NEMO), was disseminated through student chapters in Argentina, Belgium, Canada, China, Colombia, India, Latvia, Mexico, Peru, Russia, Singapore, South Africa, and the United States. We tracked the movement of this material through the network, up to the student-teacher feedback stage. The student chapter network provided rapid dissemination of the material, translation of the material into the local language, and leveraged existing chapter contacts in schools to provide an audience. We describe the student chapter network and its impact on the development of the EduKit teaching module.

Applications of inertial-sensor high-inheritance instruments to DSN precision antenna pointing

NASA Technical Reports Server (NTRS)

Goddard, R. E.

1992-01-01

Laboratory test results of the initialization and tracking performance of an existing inertial-sensor-based instrument are given. The instrument, although not primarily designed for precision antenna pointing applications, demonstrated an on-average 10-hour tracking error of several millidegrees. The system-level instrument performance is shown by analysis to be sensor limited. Simulated instrument improvements show a tracking error of less than 1 mdeg, which would provide acceptable performance, i.e., low pointing loss, for the DSN 70-m antenna sub network, operating at Ka-band (1-cm wavelength).

Applications of inertial-sensor high-inheritance instruments to DSN precision antenna pointing

NASA Technical Reports Server (NTRS)

Goddard, R. E.

1992-01-01

Laboratory test results of the initialization and tracking performance of an existing inertial-sensor-based instrument are given. The instrument, although not primarily designed for precision antenna pointing applications, demonstrated an on-average 10-hour tracking error of several millidegrees. The system-level instrument performance is shown by analysis to be sensor limited. Simulated instrument improvements show a tracking error of less than 1 mdeg, which would provide acceptable performance, i.e., low pointing loss, for the Deep Space Network 70-m antenna subnetwork, operating at Ka-band (1-cm wavelength).

Satellite-tracking and Earth dynamics research programs

NASA Technical Reports Server (NTRS)

1981-01-01

The major focus for operations during this period was the preliminary MERIT Campaign and its intensive tracking of LAGEOS for polar motion and Earth rotation studies. The data acquired from LAGEOS were used for other geophysical investigations, including studies of crustal dynamics, and Earth and ocean tides, and for the general development of precision orbit determination. The network performed regular tracking of several other retroreflector satellites including GEOS-1, GEOS-3, BE-C, and Starlette for refined determinations of station coordinates and Earth's gravity field and for studies of solid Earth dynamics.

Tracking and imaging humans on heterogeneous infrared sensor arrays for law enforcement applications

NASA Astrophysics Data System (ADS)

Feller, Steven D.; Zheng, Y.; Cull, Evan; Brady, David J.

2002-08-01

We present a plan for the integration of geometric constraints in the source, sensor and analysis levels of sensor networks. The goal of geometric analysis is to reduce the dimensionality and complexity of distributed sensor data analysis so as to achieve real-time recognition and response to significant events. Application scenarios include biometric tracking of individuals, counting and analysis of individuals in groups of humans and distributed sentient environments. We are particularly interested in using this approach to provide networks of low cost point detectors, such as infrared motion detectors, with complex imaging capabilities. By extending the capabilities of simple sensors, we expect to reduce the cost of perimeter and site security applications.

Range Measurement as Practiced in the Deep Space Network

NASA Technical Reports Server (NTRS)

Berner, Jeff B.; Bryant, Scott H.; Kinman, Peter W.

2007-01-01

Range measurements are used to improve the trajectory models of spacecraft tracked by the Deep Space Network. The unique challenge of deep-space ranging is that the two-way delay is long, typically many minutes, and the signal-to-noise ratio is small. Accurate measurements are made under these circumstances by means of long correlations that incorporate Doppler rate-aiding. This processing is done with commercial digital signal processors, providing a flexibility in signal design that can accommodate both the traditional sequential ranging signal and pseudonoise range codes. Accurate range determination requires the calibration of the delay within the tracking station. Measurements with a standard deviation of 1 m have been made.

Vegetation associated with different walking track types in the Kosciuszko alpine area, Australia.

PubMed

Hill, Wendy; Pickering, Catherine Marina

2006-01-01

Tourism infrastructure such as walking tracks can have negative effects on vegetation including in mountain regions. In the alpine area around continental Australia's highest mountain, Mt Kosciuszko (2228 m), there is a range of walking tracks (paved, gravel and raised steel mesh surfaces) in addition to an extensive network of informal/non-hardened tracks. Vegetation characteristics were compared between track types on/under tracks, on the track verge, and in the adjacent native vegetation. For a raised steel mesh walkway there was no difference in vegetation under the walkway, on the verge, and 3m away. In contrast, for a non-hardened track there was 35% bare ground on the track surface but no other detectable impacts. Gravel and paved tracks had distinct verges largely comprising bare ground and exotic species. For non-hardened tracks there was an estimated 270 m2 of disturbance per km of track. For wide gravel tracks the combined area of bare ground, exotic plants and gravel was estimated as 4290 m2 per km, while for narrow gravel tracks it was estimated as 2940 m2 per km. For paved tracks there was around 2680 m2 per km of damage. In contrast, there was no detectable effect of raised steel mesh walkway on vegetation highlighting some of the benefits of this surface over other track types.

Multiple Objects Fusion Tracker Using a Matching Network for Adaptively Represented Instance Pairs

PubMed Central

Oh, Sang-Il; Kang, Hang-Bong

2017-01-01

Multiple-object tracking is affected by various sources of distortion, such as occlusion, illumination variations and motion changes. Overcoming these distortions by tracking on RGB frames, such as shifting, has limitations because of material distortions caused by RGB frames. To overcome these distortions, we propose a multiple-object fusion tracker (MOFT), which uses a combination of 3D point clouds and corresponding RGB frames. The MOFT uses a matching function initialized on large-scale external sequences to determine which candidates in the current frame match with the target object in the previous frame. After conducting tracking on a few frames, the initialized matching function is fine-tuned according to the appearance models of target objects. The fine-tuning process of the matching function is constructed as a structured form with diverse matching function branches. In general multiple object tracking situations, scale variations for a scene occur depending on the distance between the target objects and the sensors. If the target objects in various scales are equally represented with the same strategy, information losses will occur for any representation of the target objects. In this paper, the output map of the convolutional layer obtained from a pre-trained convolutional neural network is used to adaptively represent instances without information loss. In addition, MOFT fuses the tracking results obtained from each modality at the decision level to compensate the tracking failures of each modality using basic belief assignment, rather than fusing modalities by selectively using the features of each modality. Experimental results indicate that the proposed tracker provides state-of-the-art performance considering multiple objects tracking (MOT) and KITTIbenchmarks. PMID:28420194

Poster - 51: A tumor motion-compensating system with tracking and prediction – a proof-of-concept study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guo, Kaiming; Teo, Peng; Kawalec, Philip

2016-08-15

Purpose: This work reports on the development of a mechanical slider system for the counter-steering of tumor motion in adaptive Radiation Therapy (RT). The tumor motion was tracked using a weighted optical flow algorithm and its position is being predicted with a neural network (NN). Methods: The components of the proposed mechanical counter-steering system includes: (1) an actuator which provides the tumor motion, (2) the motion detection using an optical flow algorithm, (3) motion prediction using a neural network, (4) a control module and (5) a mechanical slider to counter-steer the anticipated motion of the tumor phantom. An asymmetrical cosinemore » function and five patient traces (P1–P5) were used to evaluate the tracking of a 3D printed lung tumor. In the proposed mechanical counter-steering system, both actuator (Zaber NA14D60) and slider (Zaber A-BLQ0070-E01) were programed to move independently with LabVIEW and their positions were recorded by 2 potentiometers (ETI LCP12S-25). The accuracy of this counter-steering system is given by the difference between the two potentiometers. Results: The inherent accuracy of the system, measured using the cosine function, is −0.15 ± 0.06 mm. While the errors when tracking and prediction were included, is (0.04 ± 0.71) mm. Conclusion: A prototype tumor motion counter-steering system with tracking and prediction was implemented. The inherent errors are small in comparison to the tracking and prediction errors, which in turn are small in comparison to the magnitude of tumor motion. The results show that this system is suited for evaluating RT tracking and prediction.« less

Commercial associative memory performance for applications in track-based triggers at the Large Hadron Collider

NASA Astrophysics Data System (ADS)

Webster, Jordan

2017-01-01

Dense track environments in pp collisions at the Large Hadron Collider (LHC) motivate the use of triggers with dedicated hardware for fast track reconstruction. The ATLAS Collaboration is in the process of implementing a Fast Tracker (FTK) trigger upgrade, in which Content Addressable Memories (CAMs) will be used to rapidly match hit patterns with large banks of simulated tracks. The FTK CAMs are produced primarily at the University of Pisa. However, commercial CAM technology is rapidly developing due to applications in computer networking devices. This poster presents new studies comparing FTK CAMs to cutting-edge ternary CAMs developed by Cavium. The comparison is intended to guide the design of future track-based trigger systems for the next Phase at the LHC.

Multi-Stage Target Tracking with Drift Correction and Position Prediction

NASA Astrophysics Data System (ADS)

Chen, Xin; Ren, Keyan; Hou, Yibin

2018-04-01

Most existing tracking methods are hard to combine accuracy and performance, and do not consider the shift between clarity and blur that often occurs. In this paper, we propound a multi-stage tracking framework with two particular modules: position prediction and corrective measure. We conduct tracking based on correlation filter with a corrective measure module to increase both performance and accuracy. Specifically, a convolutional network is used for solving the blur problem in realistic scene, training methodology that training dataset with blur images generated by the three blur algorithms. Then, we propose a position prediction module to reduce the computation cost and make tracker more capable of fast motion. Experimental result shows that our tracking method is more robust compared to others and more accurate on the benchmark sequences.

Model-Based Localization and Tracking Using Bluetooth Low-Energy Beacons

PubMed Central

Cemgil, Ali Taylan

2017-01-01

We introduce a high precision localization and tracking method that makes use of cheap Bluetooth low-energy (BLE) beacons only. We track the position of a moving sensor by integrating highly unreliable and noisy BLE observations streaming from multiple locations. A novel aspect of our approach is the development of an observation model, specifically tailored for received signal strength indicator (RSSI) fingerprints: a combination based on the optimal transport model of Wasserstein distance. The tracking results of the entire system are compared with alternative baseline estimation methods, such as nearest neighboring fingerprints and an artificial neural network. Our results show that highly accurate estimation from noisy Bluetooth data is practically feasible with an observation model based on Wasserstein distance interpolation combined with the sequential Monte Carlo (SMC) method for tracking. PMID:29109375

Model-Based Localization and Tracking Using Bluetooth Low-Energy Beacons.

PubMed

Daniş, F Serhan; Cemgil, Ali Taylan

2017-10-29

We introduce a high precision localization and tracking method that makes use of cheap Bluetooth low-energy (BLE) beacons only. We track the position of a moving sensor by integrating highly unreliable and noisy BLE observations streaming from multiple locations. A novel aspect of our approach is the development of an observation model, specifically tailored for received signal strength indicator (RSSI) fingerprints: a combination based on the optimal transport model of Wasserstein distance. The tracking results of the entire system are compared with alternative baseline estimation methods, such as nearest neighboring fingerprints and an artificial neural network. Our results show that highly accurate estimation from noisy Bluetooth data is practically feasible with an observation model based on Wasserstein distance interpolation combined with the sequential Monte Carlo (SMC) method for tracking.

An adaptive recurrent neural-network controller using a stabilization matrix and predictive inputs to solve a tracking problem under disturbances.

PubMed

Fairbank, Michael; Li, Shuhui; Fu, Xingang; Alonso, Eduardo; Wunsch, Donald

2014-01-01

We present a recurrent neural-network (RNN) controller designed to solve the tracking problem for control systems. We demonstrate that a major difficulty in training any RNN is the problem of exploding gradients, and we propose a solution to this in the case of tracking problems, by introducing a stabilization matrix and by using carefully constrained context units. This solution allows us to achieve consistently lower training errors, and hence allows us to more easily introduce adaptive capabilities. The resulting RNN is one that has been trained off-line to be rapidly adaptive to changing plant conditions and changing tracking targets. The case study we use is a renewable-energy generator application; that of producing an efficient controller for a three-phase grid-connected converter. The controller we produce can cope with the random variation of system parameters and fluctuating grid voltages. It produces tracking control with almost instantaneous response to changing reference states, and virtually zero oscillation. This compares very favorably to the classical proportional integrator (PI) controllers, which we show produce a much slower response and settling time. In addition, the RNN we propose exhibits better learning stability and convergence properties, and can exhibit faster adaptation, than has been achieved with adaptive critic designs. Copyright © 2013 Elsevier Ltd. All rights reserved.

A decade of environmental public health tracking (2002-2012): progress and challenges.

PubMed

Kearney, Gregory D; Namulanda, Gonza; Qualters, Judith R; Talbott, Evelyn O

2015-01-01

The creation of the Centers for Disease Control and Prevention Environmental Public Health Tracking Program spawned an invigorating and challenging approach toward implementing the nation's first population-based, environmental disease tracking surveillance system. More than 10 years have passed since its creation and an abundance of peer-reviewed articles have been published spanning a broad variety of public health topics related primarily to the goal of reducing diseases of environmental origin. To evaluate peer-reviewed literature related to Environmental Public Health Tracking during 2002-2012, recognize major milestones and challenges, and offer recommendations. A narrative overview was conducted using titles and abstracts of peer-reviewed articles, key word searches, and science-based search engine databases. Eighty published articles related to "health tracking" were identified and categorized according to 4 crossed-central themes. The Science and Research theme accounted for the majority of published articles, followed by Policy and Practice, Collaborations Among Health and Environmental Programs, and Network Development. Overall, progress was reported in the areas of data linkage, data sharing, surveillance methods, and network development. Ongoing challenges included formulating better ways to establish the connections between health and the environment, such as using biomonitoring, public water systems, and private well water data. Recommendations for future efforts include use of data to inform policy and practice and use of electronic health records data for environmental health surveillance.

Robust leader-follower formation tracking control of multiple underactuated surface vessels

NASA Astrophysics Data System (ADS)

Peng, Zhou-hua; Wang, Dan; Lan, Wei-yao; Sun, Gang

2012-09-01

This paper is concerned with the formation control problem of multiple underactuated surface vessels moving in a leader-follower formation. The formation is achieved by the follower to track a virtual target defined relative to the leader. A robust adaptive target tracking law is proposed by using neural network and backstepping techniques. The advantage of the proposed control scheme is that the uncertain nonlinear dynamics caused by Coriolis/centripetal forces, nonlinear damping, unmodeled hydrodynamics and disturbances from the environment can be compensated by on line learning. Based on Lyapunov analysis, the proposed controller guarantees the tracking errors converge to a small neighborhood of the origin. Simulation results demonstrate the effectiveness of the control strategy.

Noncoherent Doppler tracking: first flight results

NASA Astrophysics Data System (ADS)

DeBoy, Christopher C.; Robert Jensen, J.; Asher, Mark S.

2005-01-01

Noncoherent Doppler tracking has been devised as a means to achieve highly accurate, two-way Doppler measurements with a simple, transceiver-based communications system. This technique has been flown as an experiment on the Thermosphere, Ionosphere, Mesosphere, Energetics and Dynamics (TIMED) spacecraft, (launched 7 December 2001), as the operational technique for Doppler tracking on CONTOUR, and is baselined on several future deep space missions at JHU/APL. This paper reports on initial results from a series of successful tests of this technique between the TIMED spacecraft and NASA ground stations in the Deep Space Network. It also examines the advantages that noncoherent Doppler tracking and a transceiver-based system may offer to small satellite systems, including reduced cost, mass, and power.

A new test method for the assessment of the arc tracking properties of wire insulation in air, oxygen enriched atmospheres and vacuum

NASA Technical Reports Server (NTRS)

Koenig, Dieter

1994-01-01

Development of a new test method suitable for the assessment of the resistance of aerospace cables to arc tracking for different specific environmental and network conditions of spacecraft is given in view-graph format. The equipment can be easily adapted for tests at different realistic electrical network conditions incorporating circuit protection and the test system works equally well whatever the test atmosphere. Test results confirm that pure Kapton insulated wire has bad arcing characteristics and ETFE insulated wire is considerably better in air. For certain wires, arc tracking effects are increased at higher oxygen concentrations and significantly increased under vacuum. All tests on different cable insulation materials and in different environments, including enriched oxygen atmospheres, resulted in a more or less rapid extinguishing of all high temperature effects at the beginning of the post-test phase. In no case was a self-maintained fire initiated by the arc.

Multicell migration tracking within angiogenic networks by deep learning-based segmentation and augmented Bayesian filtering.

PubMed

Wang, Mengmeng; Ong, Lee-Ling Sharon; Dauwels, Justin; Asada, H Harry

2018-04-01

Cell migration is a key feature for living organisms. Image analysis tools are useful in studying cell migration in three-dimensional (3-D) in vitro environments. We consider angiogenic vessels formed in 3-D microfluidic devices (MFDs) and develop an image analysis system to extract cell behaviors from experimental phase-contrast microscopy image sequences. The proposed system initializes tracks with the end-point confocal nuclei coordinates. We apply convolutional neural networks to detect cell candidates and combine backward Kalman filtering with multiple hypothesis tracking to link the cell candidates at each time step. These hypotheses incorporate prior knowledge on vessel formation and cell proliferation rates. The association accuracy reaches 86.4% for the proposed algorithm, indicating that the proposed system is able to associate cells more accurately than existing approaches. Cell culture experiments in 3-D MFDs have shown considerable promise for improving biology research. The proposed system is expected to be a useful quantitative tool for potential microscopy problems of MFDs.

Earth's gravity field to the eighteenth degree and geocentric coordinates for 104 stations from satellite and terrestrial data

NASA Technical Reports Server (NTRS)

Gaposchkin, E. M.

1973-01-01

Geodetic parameters describing the earth's gravity field and the positions of satellite-tracking stations in a geocentric reference frame were computed. These parameters were estimated by means of a combination of five different types of data: routine and simultaneous satellite observations, observations of deep-space probes, measurements of terrestrial gravity, and surface-triangulation data. The combination gives better parameters than does any subset of data types. The dynamic solution used precision-reduced Baker-Nunn observations and laser range data of 25 satellites. Data from the 49-station National Oceanic and Atmospheric Administration BC-4 network, the 19-station Smithsonian Astrophysical Observatory Baker-Nunn network, and independent camera stations were employed in the geometrical solution. Data from the tracking of deep-space probes were converted to relative longitudes and distances to the earth's axis of rotation of the tracking stations. Surface-gravity data in the form of 550-km squares were derived from 19,328 1 deg X 1 deg mean gravity anomalies.

Orbit Determination for the Lunar Reconnaissance Orbiter Using an Extended Kalman Filter

NASA Technical Reports Server (NTRS)

Slojkowski, Steven; Lowe, Jonathan; Woodburn, James

2015-01-01

Since launch, the FDF has performed daily OD for LRO using the Goddard Trajectory Determination System (GTDS). GTDS is a batch least-squares (BLS) estimator. The tracking data arc for OD is 36 hours. Current operational OD uses 200 x 200 lunar gravity, solid lunar tides, solar radiation pressure (SRP) using a spherical spacecraft area model, and point mass gravity for the Earth, Sun, and Jupiter. LRO tracking data consists of range and range-rate measurements from: Universal Space Network (USN) stations in Sweden, Germany, Australia, and Hawaii. A NASA antenna at White Sands, New Mexico (WS1S). NASA Deep Space Network (DSN) stations. DSN data was sparse and not included in this study. Tracking is predominantly (50) from WS1S. The OD accuracy requirements are: Definitive ephemeris accuracy of 500 meters total position root-mean-squared (RMS) and18 meters radial RMS. Predicted orbit accuracy less than 800 meters root sum squared (RSS) over an 84-hour prediction span.

Topological Vulnerability Analysis

NASA Astrophysics Data System (ADS)

Jajodia, Sushil; Noel, Steven

Traditionally, network administrators rely on labor-intensive processes for tracking network configurations and vulnerabilities. This requires a great deal of expertise, and is error prone because of the complexity of networks and associated security data. The interdependencies of network vulnerabilities make traditional point-wise vulnerability analysis inadequate. We describe a Topological Vulnerability Analysis (TVA) approach that analyzes vulnerability dependencies and shows all possible attack paths into a network. From models of the network vulnerabilities and potential attacker exploits, we compute attack graphs that convey the impact of individual and combined vulnerabilities on overall security. TVA finds potential paths of vulnerability through a network, showing exactly how attackers may penetrate a network. From this, we identify key vulnerabilities and provide strategies for protection of critical network assets.

Neural mechanisms tracking popularity in real-world social networks

PubMed Central

Zerubavel, Noam; Bearman, Peter S.; Weber, Jochen; Ochsner, Kevin N.

2015-01-01

Differences in popularity are a key aspect of status in virtually all human groups and shape social interactions within them. Little is known, however, about how we track and neurally represent others’ popularity. We addressed this question in two real-world social networks using sociometric methods to quantify popularity. Each group member (perceiver) viewed faces of every other group member (target) while whole-brain functional MRI data were collected. Independent functional localizer tasks were used to identify brain systems supporting affective valuation (ventromedial prefrontal cortex, ventral striatum, amygdala) and social cognition (dorsomedial prefrontal cortex, precuneus, temporoparietal junction), respectively. During the face-viewing task, activity in both types of neural systems tracked targets’ sociometric popularity, even when controlling for potential confounds. The target popularity–social cognition system relationship was mediated by valuation system activity, suggesting that observing popular individuals elicits value signals that facilitate understanding their mental states. The target popularity–valuation system relationship was strongest for popular perceivers, suggesting enhanced sensitivity to differences among other group members’ popularity. Popular group members also demonstrated greater interpersonal sensitivity by more accurately predicting how their own personalities were perceived by other individuals in the social network. These data offer insights into the mechanisms by which status guides social behavior. PMID:26598684

Enhanced robust fractional order proportional-plus-integral controller based on neural network for velocity control of permanent magnet synchronous motor.

PubMed

Zhang, Bitao; Pi, YouGuo

2013-07-01

The traditional integer order proportional-integral-differential (IO-PID) controller is sensitive to the parameter variation or/and external load disturbance of permanent magnet synchronous motor (PMSM). And the fractional order proportional-integral-differential (FO-PID) control scheme based on robustness tuning method is proposed to enhance the robustness. But the robustness focuses on the open-loop gain variation of controlled plant. In this paper, an enhanced robust fractional order proportional-plus-integral (ERFOPI) controller based on neural network is proposed. The control law of the ERFOPI controller is acted on a fractional order implement function (FOIF) of tracking error but not tracking error directly, which, according to theory analysis, can enhance the robust performance of system. Tuning rules and approaches, based on phase margin, crossover frequency specification and robustness rejecting gain variation, are introduced to obtain the parameters of ERFOPI controller. And the neural network algorithm is used to adjust the parameter of FOIF. Simulation and experimental results show that the method proposed in this paper not only achieve favorable tracking performance, but also is robust with regard to external load disturbance and parameter variation. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Robust Hidden Markov Model based intelligent blood vessel detection of fundus images.

PubMed

Hassan, Mehdi; Amin, Muhammad; Murtza, Iqbal; Khan, Asifullah; Chaudhry, Asmatullah

2017-11-01

In this paper, we consider the challenging problem of detecting retinal vessel networks. Precise detection of retinal vessel networks is vital for accurate eye disease diagnosis. Most of the blood vessel tracking techniques may not properly track vessels in presence of vessels' occlusion. Owing to problem in sensor resolution or acquisition of fundus images, it is possible that some part of vessel may occlude. In this scenario, it becomes a challenging task to accurately trace these vital vessels. For this purpose, we have proposed a new robust and intelligent retinal vessel detection technique on Hidden Markov Model. The proposed model is able to successfully track vessels in the presence of occlusion. The effectiveness of the proposed technique is evaluated on publically available standard DRIVE dataset of the fundus images. The experiments show that the proposed technique not only outperforms the other state of the art methodologies of retinal blood vessels segmentation, but it is also capable of accurate occlusion handling in retinal vessel networks. The proposed technique offers better average classification accuracy, sensitivity, specificity, and area under the curve (AUC) of 95.7%, 81.0%, 97.0%, and 90.0% respectively, which shows the usefulness of the proposed technique. Copyright © 2017 Elsevier B.V. All rights reserved.

MEASUREMENT OF RURAL SULFUR DIOXIDE AND PARTICLE SULFATE: ANALYSIS OF CASTNET DATA, 1987 - 1996

EPA Science Inventory

The Clean Sir Status and Trends Network (CASTNet) was implemented by the U.S. Environmental Protection Agency (EPA) in 1991 in response to Title IX of the Clean Air Amendments of 1990, which mandated the deployment of a national ambient air monitoring network to track progress of...

Speaker-dependent Multipitch Tracking Using Deep Neural Networks

DTIC Science & Technology

2015-01-01

connections through time. Studies have shown that RNNs are good at modeling sequential data like handwriting [12] and speech [26]. We plan to explore RNNs in...Schmidhuber, and S. Fernández, “Unconstrained on-line handwriting recognition with recurrent neural networks,” in Proceedings of NIPS, 2008, pp. 577–584. [13

RadNet Map Interface for Near-Real-Time Radiation Monitoring Data

EPA Pesticide Factsheets

RadNet is a national network of monitoring stations that regularly collect air, precipitation, drinking water, and milk samples for analysis of radioactivity. The RadNet network, which has stations in each state, has been used to track environmental releases of radioactivity from nuclear weapons tests and nuclear accidents.

RadNet Air Quality (Fixed Station) Data

EPA Pesticide Factsheets

RadNet is a national network of monitoring stations that regularly collect air for analysis of radioactivity. The RadNet network, which has stations in each State, has been used to track environmental releases of radioactivity from nuclear weapons tests and nuclear accidents. RadNet also documents the status and trends of environmental radioactivity

SLR tracking of GPS-35

NASA Technical Reports Server (NTRS)

Pavlis, Erricos C.

1994-01-01

An experiment was designed to launch a corner cube retroreflector array on one of the Global Positioning Satellites (GPS). The launch on Aug. 31, 1993 ushered in the era of SLR tracking of GPS spacecraft. Once the space operations group finished the check-out procedures for the new satellite, the agreed upon SLR sites were allowed to track it. The first site to acquire GPS-35 was the Russian system at Maidanak and closely after the MLRS system at McDonald Observatory, Texas. The laser tracking network is currently tracking the GPS spacecraft known as GPS-35 or PRN 5 with great success. From the NASA side there are five stations that contribute data regularly and nearly as many from the international partners. Upcoming modifications to the ground receivers will allow for a further increase in the tracking capabilities of several additional sites and add some desperately needed southern hemisphere tracking. We are analyzing the data and are comparing SLR-derived orbits to those determined on the basis of GPS radiometric data.

Geomorphic and biophysical factors affecting water tracks in northern Alaska

NASA Astrophysics Data System (ADS)

Trochim, E. D.; Jorgenson, M. T.; Prakash, A.; Kane, D. L.

2016-03-01

A better understanding of water movement on hillslopes in Arctic environments is necessary for evaluating the effects of climate variability. Drainage networks include a range of features that vary in transport capacity from rills to water tracks to rivers. This research focuses on describing and classifying water tracks, which are saturated linear-curvilinear stripes that act as first-order pathways for transporting water off of hillslopes into valley bottoms and streams. Multiple factor analysis was used to develop five water tracks classes based on their geomorphic, soil, and vegetation characteristics. The water track classes were then validated using conditional inference trees, to verify that the classes were repeatable. Analysis of the classes and their characteristics indicate that water tracks cover a broad spectrum of patterns and processes primarily driven by surficial geology. This research demonstrates an improved approach to quantifying water track characteristics for specific areas, which is a major step toward understanding hydrological processes and feedbacks within a region.

Weed or wheel! FMRI, behavioural, and toxicological investigations of how cannabis smoking affects skills necessary for driving.

PubMed

Battistella, Giovanni; Fornari, Eleonora; Thomas, Aurélien; Mall, Jean-Frédéric; Chtioui, Haithem; Appenzeller, Monique; Annoni, Jean-Marie; Favrat, Bernard; Maeder, Philippe; Giroud, Christian

2013-01-01

Marijuana is the most widely used illicit drug, however its effects on cognitive functions underlying safe driving remain mostly unexplored. Our goal was to evaluate the impact of cannabis on the driving ability of occasional smokers, by investigating changes in the brain network involved in a tracking task. The subject characteristics, the percentage of Δ(9)-Tetrahydrocannabinol in the joint, and the inhaled dose were in accordance with real-life conditions. Thirty-one male volunteers were enrolled in this study that includes clinical and toxicological aspects together with functional magnetic resonance imaging of the brain and measurements of psychomotor skills. The fMRI paradigm was based on a visuo-motor tracking task, alternating active tracking blocks with passive tracking viewing and rest condition. We show that cannabis smoking, even at low Δ(9)-Tetrahydrocannabinol blood concentrations, decreases psychomotor skills and alters the activity of the brain networks involved in cognition. The relative decrease of Blood Oxygen Level Dependent response (BOLD) after cannabis smoking in the anterior insula, dorsomedial thalamus, and striatum compared to placebo smoking suggests an alteration of the network involved in saliency detection. In addition, the decrease of BOLD response in the right superior parietal cortex and in the dorsolateral prefrontal cortex indicates the involvement of the Control Executive network known to operate once the saliencies are identified. Furthermore, cannabis increases activity in the rostral anterior cingulate cortex and ventromedial prefrontal cortices, suggesting an increase in self-oriented mental activity. Subjects are more attracted by intrapersonal stimuli ("self") and fail to attend to task performance, leading to an insufficient allocation of task-oriented resources and to sub-optimal performance. These effects correlate with the subjective feeling of confusion rather than with the blood level of Δ(9)-Tetrahydrocannabinol. These findings bolster the zero-tolerance policy adopted in several countries that prohibits the presence of any amount of drugs in blood while driving.

Weed or Wheel! fMRI, Behavioural, and Toxicological Investigations of How Cannabis Smoking Affects Skills Necessary for Driving

PubMed Central

Thomas, Aurélien; Mall, Jean-Frédéric; Chtioui, Haithem; Appenzeller, Monique; Annoni, Jean-Marie; Favrat, Bernard

2013-01-01

Marijuana is the most widely used illicit drug, however its effects on cognitive functions underling safe driving remain mostly unexplored. Our goal was to evaluate the impact of cannabis on the driving ability of occasional smokers, by investigating changes in the brain network involved in a tracking task. The subject characteristics, the percentage of Δ9-Tetrahydrocannabinol in the joint, and the inhaled dose were in accordance with real-life conditions. Thirty-one male volunteers were enrolled in this study that includes clinical and toxicological aspects together with functional magnetic resonance imaging of the brain and measurements of psychomotor skills. The fMRI paradigm was based on a visuo-motor tracking task, alternating active tracking blocks with passive tracking viewing and rest condition. We show that cannabis smoking, even at low Δ9-Tetrahydrocannabinol blood concentrations, decreases psychomotor skills and alters the activity of the brain networks involved in cognition. The relative decrease of Blood Oxygen Level Dependent response (BOLD) after cannabis smoking in the anterior insula, dorsomedial thalamus, and striatum compared to placebo smoking suggests an alteration of the network involved in saliency detection. In addition, the decrease of BOLD response in the right superior parietal cortex and in the dorsolateral prefrontal cortex indicates the involvement of the Control Executive network known to operate once the saliencies are identified. Furthermore, cannabis increases activity in the rostral anterior cingulate cortex and ventromedial prefrontal cortices, suggesting an increase in self-oriented mental activity. Subjects are more attracted by intrapersonal stimuli (“self”) and fail to attend to task performance, leading to an insufficient allocation of task-oriented resources and to sub-optimal performance. These effects correlate with the subjective feeling of confusion rather than with the blood level of Δ9-Tetrahydrocannabinol. These findings bolster the zero-tolerance policy adopted in several countries that prohibits the presence of any amount of drugs in blood while driving. PMID:23300977

Considerations for multiple hypothesis correlation on tactical platforms

NASA Astrophysics Data System (ADS)

Thomas, Alan M.; Turpen, James E.

2013-05-01

Tactical platforms benefit greatly from the fusion of tracks from multiple sources in terms of increased situation awareness. As a necessary precursor to this track fusion, track-to-track association, or correlation, must first be performed. The related measurement-to-track fusion problem has been well studied with multiple hypothesis tracking and multiple frame assignment methods showing the most success. The track-to-track problem differs from this one in that measurements themselves are not available but rather track state update reports from the measuring sensors. Multiple hypothesis, multiple frame correlation systems have previously been considered; however, their practical implementation under the constraints imposed by tactical platforms is daunting. The situation is further exacerbated by the inconvenient nature of reports from legacy sensor systems on bandwidth- limited communications networks. In this paper, consideration is given to the special difficulties encountered when attempting the correlation of tracks from legacy sensors on tactical aircraft. Those difficulties include the following: covariance information from reporting sensors is frequently absent or incomplete; system latencies can create temporal uncertainty in data; and computational processing is severely limited by hardware and architecture. Moreover, consideration is given to practical solutions for dealing with these problems in a multiple hypothesis correlator.

Can Social Networks Assist Analysts Fight Terrorism?

DTIC Science & Technology

2011-06-01

protecting America from terrorism in a 2007 article. He proposed the intelligence community ought to build a social networking database to track... filming the event, mostly with mobile phones (Shirky 2009). BBC and the U.S. Geological Survey agencies learned of the event from Twitter minutes...North America , Facebook has over 500,000 unique users visit its site every month (eBizMBA 2011). Third only to QQ, China’s top social network, and Skype

Test of Neural Network Techniques using Simulated Dual-Band Data of LEO Satellites

DTIC Science & Technology

2010-09-01

resolved images of satellites are unavailable[1]. Neural networks have been evaluated as a potential automated technique for identifying satellites in...neural network, multiple photometric measurements must be made for each satellite under similar observational conditions. At the same time , this set...are compared to values posted in a real- time satellite tracking website[6]. Agreement to within 0.01 degrees in latitude and longitude and ~100 meters

Realizing the Potential of Information Resources: Information, Technology, and Services. Proceedings of the CAUSE Annual Conference (New Orleans, Louisiana, November 28-December 3, 1995).

ERIC Educational Resources Information Center

CAUSE, Boulder, CO.

This document presents the proceedings of a conference on managing and using information technology in higher education in regard to client/server computing, network delivery, process reengineering, leveraging of resources, and professional development. Eight tracks, with eight papers in each track, addressed the themes of: (1) strategic planning;…

Automated Target Acquisition, Recognition and Tracking (ATTRACT). Phase 1

NASA Technical Reports Server (NTRS)

Abdallah, Mahmoud A.

1995-01-01

The primary objective of phase 1 of this research project is to conduct multidisciplinary research that will contribute to fundamental scientific knowledge in several of the USAF critical technology areas. Specifically, neural networks, signal processing techniques, and electro-optic capabilities are utilized to solve problems associated with automated target acquisition, recognition, and tracking. To accomplish the stated objective, several tasks have been identified and were executed.

Development of a real time multiple target, multi camera tracker for civil security applications

NASA Astrophysics Data System (ADS)

Åkerlund, Hans

2009-09-01

A surveillance system has been developed that can use multiple TV-cameras to detect and track personnel and objects in real time in public areas. The document describes the development and the system setup. The system is called NIVS Networked Intelligent Video Surveillance. Persons in the images are tracked and displayed on a 3D map of the surveyed area.

Gravity model improvement using the DORIS tracking system on the SPOT 2 satellite

NASA Technical Reports Server (NTRS)

Nerem, R. S.; Lerch, F. J.; Williamson, R. G.; Klosko, S. M.; Robbins, J. W.; Patel, G. B.

1994-01-01

A high-precision radiometric satellite tracking system, Doppler Orbitography and Radio-positioning Integrated by Satellite system (DORIS), has recently been developed by the French space agency, Centre National d'Etudes Spatiales (CNES). DORIS was designed to provide tracking support for missions such as the joint United States/French TOPEX/Poseidon. As part of the flight testing process, a DORIS package was flown on the French SPOT 2 satellite. A substantial quantity of geodetic quality tracking data was obtained on SPOT 2 from an extensive international DORIS tracking network. These data were analyzed to assess their accuracy and to evaluate the gravitational modeling enhancements provided by these data in combination with the Goddard Earth Model-T3 (GEM-T3) gravitational model. These observations have noise levels of 0.4 to 0.5 mm/s, with few residual systematic effects. Although the SPOT 2 satellite experiences high atmospheric drag forces, the precision and global coverage of the DORIS tracking data have enabled more extensive orbit parameterization to mitigate these effects. As a result, the SPOT 2 orbital errors have been reduced to an estimated radial accuracy in the 10-20 cm RMS range. The addition of these data, which encompass many regions heretofore lacking in precision satellite tracking, has significantly improved GEM-T3 and allowed greatly improved orbit accuracies for Sun-synchronous satellites like SPOT 2 (such as ERS 1 and EOS). Comparison of the ensuing gravity model with other contemporary fields (GRIM-4C2, TEG2B, and OSU91A) provides a means to assess the current state of knowledge of the Earth's gravity field. Thus, the DORIS experiment on SPOT 2 has provided a strong basis for evaluating this new orbit tracking technology and has demonstrated the important contribution of the DORIS network to the success of the TOPEX/Poseidon mission.

Space debris tracking based on fuzzy running Gaussian average adaptive particle filter track-before-detect algorithm

NASA Astrophysics Data System (ADS)

Torteeka, Peerapong; Gao, Peng-Qi; Shen, Ming; Guo, Xiao-Zhang; Yang, Da-Tao; Yu, Huan-Huan; Zhou, Wei-Ping; Zhao, You

2017-02-01

Although tracking with a passive optical telescope is a powerful technique for space debris observation, it is limited by its sensitivity to dynamic background noise. Traditionally, in the field of astronomy, static background subtraction based on a median image technique has been used to extract moving space objects prior to the tracking operation, as this is computationally efficient. The main disadvantage of this technique is that it is not robust to variable illumination conditions. In this article, we propose an approach for tracking small and dim space debris in the context of a dynamic background via one of the optical telescopes that is part of the space surveillance network project, named the Asia-Pacific ground-based Optical Space Observation System or APOSOS. The approach combines a fuzzy running Gaussian average for robust moving-object extraction with dim-target tracking using a particle-filter-based track-before-detect method. The performance of the proposed algorithm is experimentally evaluated, and the results show that the scheme achieves a satisfactory level of accuracy for space debris tracking.

Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods.

PubMed

Hoak, Anthony; Medeiros, Henry; Povinelli, Richard J

2017-03-03

We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, Australian Rules Football League (AFL) and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (optimal sub-pattern assignment (OSPA) and classification of events, activities and relationships for multi-object trackers (CLEAR MOT)). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods

PubMed Central

Hoak, Anthony; Medeiros, Henry; Povinelli, Richard J.

2017-01-01

We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, Australian Rules Football League (AFL) and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (optimal sub-pattern assignment (OSPA) and classification of events, activities and relationships for multi-object trackers (CLEAR MOT)). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter. PMID:28273796

The minitrack tracking function description, volume 1

NASA Technical Reports Server (NTRS)

Englar, T. S., Jr.; Mango, S. A.; Roettcher, C. A.; Watters, D. L.

1973-01-01

The treatment of tracking data by the Minitrack system is described from the transmission of the nominal 136-MHz radio beacon energy from a satellite and the reception of this signal by the interferometer network through the ultimate derivation of the direction cosines (the angular coordinates of the vector from the tracking station to the spacecraft) as a function of time. Descriptions of some of the lesser-known functions operating on the system, such as the computer preprocessing program, are included. A large part of the report is devoted to the preprocessor, which provides for the data compression, smoothing, calibration correction, and ambiguity resolution of the raw interferometer phase tracking measurements teletyped from each of the worldwide Minitrack tracking stations to the central computer facility at Goddard Space Flight Center. An extensive bibliography of Minitrack hardware and theory is presented.

A neurocomputational model of figure-ground discrimination and target tracking.

PubMed

Sun, H; Liu, L; Guo, A

1999-01-01

A neurocomputational model is presented for figureground discrimination and target tracking. In the model, the elementary motion detectors of the correlation type, the computational modules of saccadic and smooth pursuit eye movement, an oscillatory neural-network motion perception module and a selective attention module are involved. It is shown that through the oscillatory amplitude and frequency encoding, and selective synchronization of phase oscillators, the figure and the ground can be successfully discriminated from each other. The receptive fields developed by hidden units of the networks were surprisingly similar to the actual receptive fields and columnar organization found in the primate visual cortex. It is suggested that equivalent mechanisms may exist in the primate visual cortex to discriminate figure-ground in both temporal and spatial domains.

Some issues related to simulation of the tracking and communications computer network

NASA Technical Reports Server (NTRS)

Lacovara, Robert C.

1989-01-01

The Communications Performance and Integration branch of the Tracking and Communications Division has an ongoing involvement in the simulation of its flight hardware for Space Station Freedom. Specifically, the communication process between central processor(s) and orbital replaceable units (ORU's) is simulated with varying degrees of fidelity. The results of investigations into three aspects of this simulation effort are given. The most general area involves the use of computer assisted software engineering (CASE) tools for this particular simulation. The second area of interest is simulation methods for systems of mixed hardware and software. The final area investigated is the application of simulation methods to one of the proposed computer network protocols for space station, specifically IEEE 802.4.

Evaluation of a Pilot Surveillance System: Health and Environment Linked for Information Exchange in Atlanta (HELIX-Atlanta)

NASA Technical Reports Server (NTRS)

Meyer, P.; Shire, J.; Qualters, Judy; Daley, Randolph; Fiero, Leslie Todorov; Autry, Andy; Avchen, Rachel; Stock, Allison; Correa, Adolofo; Siffel, Csaba;

Some issues related to simulation of the tracking and communications computer network

NASA Astrophysics Data System (ADS)

Lacovara, Robert C.

1989-12-01

The Communications Performance and Integration branch of the Tracking and Communications Division has an ongoing involvement in the simulation of its flight hardware for Space Station Freedom. Specifically, the communication process between central processor(s) and orbital replaceable units (ORU's) is simulated with varying degrees of fidelity. The results of investigations into three aspects of this simulation effort are given. The most general area involves the use of computer assisted software engineering (CASE) tools for this particular simulation. The second area of interest is simulation methods for systems of mixed hardware and software. The final area investigated is the application of simulation methods to one of the proposed computer network protocols for space station, specifically IEEE 802.4.

Tracking and recognition of multiple human targets moving in a wireless pyroelectric infrared sensor network.

PubMed

Xiong, Ji; Li, Fangmin; Zhao, Ning; Jiang, Na

2014-04-22

With characteristics of low-cost and easy deployment, the distributed wireless pyroelectric infrared sensor network has attracted extensive interest, which aims to make it an alternate infrared video sensor in thermal biometric applications for tracking and identifying human targets. In these applications, effectively processing signals collected from sensors and extracting the features of different human targets has become crucial. This paper proposes the application of empirical mode decomposition and the Hilbert-Huang transform to extract features of moving human targets both in the time domain and the frequency domain. Moreover, the support vector machine is selected as the classifier. The experimental results demonstrate that by using this method the identification rates of multiple moving human targets are around 90%.

Anomaly detection driven active learning for identifying suspicious tracks and events in WAMI video

NASA Astrophysics Data System (ADS)

Miller, David J.; Natraj, Aditya; Hockenbury, Ryler; Dunn, Katherine; Sheffler, Michael; Sullivan, Kevin

2012-06-01

We describe a comprehensive system for learning to identify suspicious vehicle tracks from wide-area motion (WAMI) video. First, since the road network for the scene of interest is assumed unknown, agglomerative hierarchical clustering is applied to all spatial vehicle measurements, resulting in spatial cells that largely capture individual road segments. Next, for each track, both at the cell (speed, acceleration, azimuth) and track (range, total distance, duration) levels, extreme value feature statistics are both computed and aggregated, to form summary (p-value based) anomaly statistics for each track. Here, to fairly evaluate tracks that travel across different numbers of spatial cells, for each cell-level feature type, a single (most extreme) statistic is chosen, over all cells traveled. Finally, a novel active learning paradigm, applied to a (logistic regression) track classifier, is invoked to learn to distinguish suspicious from merely anomalous tracks, starting from anomaly-ranked track prioritization, with ground-truth labeling by a human operator. This system has been applied to WAMI video data (ARGUS), with the tracks automatically extracted by a system developed in-house at Toyon Research Corporation. Our system gives promising preliminary results in highly ranking as suspicious aerial vehicles, dismounts, and traffic violators, and in learning which features are most indicative of suspicious tracks.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1986-01-01

This publication, one of a series formerly titled The Deep Space Network (DSN) Progress Report, documents DSN progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations. In addition, developments in Earth-based radio technology as applied to geodynamics, astrophysics, and the radio search for extraterrestrial intelligence are reported.

The Telecommunications and Data Acquisition Report

NASA Technical Reports Server (NTRS)

Posner, E. C. (Editor)

1988-01-01

This publication, one of a series formerly titled The Deep Space Network Progress Report, documents DSN progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations. In addition, developments in earth-based radio technology as applied to geodynamics, astrophysics, and the radio search for extraterrestrial intelligence are reported.

Water Network Tool for Resilience v. 1.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

2015-12-09

WNTR is a python package designed to simulate and analyze resilience of water distribution networks. The software includes: - Pressure driven and demand driven hydraulic simulation - Water quality simulation to track concentration, trace, and water age - Conditional controls to simulate power outages - Models to simulate pipe breaks - A wide range of resilience metrics - Analysis and visualization tools

Temperature control simulation for a microwave transmitter cooling system. [deep space network

NASA Technical Reports Server (NTRS)

Yung, C. S.

1980-01-01

The thermal performance of a temperature control system for the antenna microwave transmitter (klystron tube) of the Deep Space Network antenna tracking system is discussed. In particular the mathematical model is presented along with the details of a computer program which is written for the system simulation and the performance parameterization. Analytical expressions are presented.

Scientific Networks on Data Landscapes: Question Difficulty, Epistemic Success, and Convergence

PubMed Central

Grim, Patrick; Singer, Daniel J.; Fisher, Steven; Bramson, Aaron; Berger, William J.; Reade, Christopher; Flocken, Carissa; Sales, Adam

2014-01-01

A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and others’, in order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes. Here we introduce a measure on the landscape meant to capture some aspects of the difficulty of answering an empirical question. We then investigate both how different communication networks affect whether the community finds the best answer and the time it takes for the community to reach consensus on an answer. We measure these two epistemic desiderata on a continuum of networks sampled from the Watts-Strogatz spectrum. It turns out that finding the best answer and reaching consensus exhibit radically different patterns. The time it takes for a community to reach a consensus in these models roughly tracks mean path length in the network. Whether a scientific community finds the best answer, on the other hand, tracks neither mean path length nor clustering coefficient. PMID:24683416

Scientific Networks on Data Landscapes: Question Difficulty, Epistemic Success, and Convergence.

PubMed

Grim, Patrick; Singer, Daniel J; Fisher, Steven; Bramson, Aaron; Berger, William J; Reade, Christopher; Flocken, Carissa; Sales, Adam

2013-12-01

A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and others', in order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes. Here we introduce a measure on the landscape meant to capture some aspects of the difficulty of answering an empirical question. We then investigate both how different communication networks affect whether the community finds the best answer and the time it takes for the community to reach consensus on an answer. We measure these two epistemic desiderata on a continuum of networks sampled from the Watts-Strogatz spectrum. It turns out that finding the best answer and reaching consensus exhibit radically different patterns. The time it takes for a community to reach a consensus in these models roughly tracks mean path length in the network. Whether a scientific community finds the best answer, on the other hand, tracks neither mean path length nor clustering coefficient.

Reinforcement learning design-based adaptive tracking control with less learning parameters for nonlinear discrete-time MIMO systems.

PubMed

Liu, Yan-Jun; Tang, Li; Tong, Shaocheng; Chen, C L Philip; Li, Dong-Juan

2015-01-01

Based on the neural network (NN) approximator, an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output (MIMO) nonlinear discrete-time systems with unknown functions and disturbances. In the design procedure, two networks are provided where one is an action network to generate an optimal control signal and the other is a critic network to approximate the cost function. An optimal control signal and adaptation laws can be generated based on two NNs. In the previous approaches, the weights of critic and action networks are updated based on the gradient descent rule and the estimations of optimal weight vectors are directly adjusted in the design. Consequently, compared with the existing results, the main contributions of this paper are: 1) only two parameters are needed to be adjusted, and thus the number of the adaptation laws is smaller than the previous results and 2) the updating parameters do not depend on the number of the subsystems for MIMO systems and the tuning rules are replaced by adjusting the norms on optimal weight vectors in both action and critic networks. It is proven that the tracking errors, the adaptation laws, and the control inputs are uniformly bounded using Lyapunov analysis method. The simulation examples are employed to illustrate the effectiveness of the proposed algorithm.

Monitoring and detection platform to prevent anomalous situations in home care.

PubMed

Villarrubia, Gabriel; Bajo, Javier; De Paz, Juan F; Corchado, Juan M

2014-06-05

Monitoring and tracking people at home usually requires high cost hardware installations, which implies they are not affordable in many situations. This study/paper proposes a monitoring and tracking system for people with medical problems. A virtual organization of agents based on the PANGEA platform, which allows the easy integration of different devices, was created for this study. In this case, a virtual organization was implemented to track and monitor patients carrying a Holter monitor. The system includes the hardware and software required to perform: ECG measurements, monitoring through accelerometers and WiFi networks. Furthermore, the use of interactive television can moderate interactivity with the user. The system makes it possible to merge the information and facilitates patient tracking efficiently with low cost.

Tracking and Data System Support for the Mariner Venus/Mercury 1973 Project

NASA Technical Reports Server (NTRS)

Davis, E. K.; Traxler, M. R.

1977-01-01

The Tracking and Data System, which provided outstanding support to the Mariner Venus/Mercury 1973 project during the period from January 1970 through March 1975 are chronologically described. In the Tracking and Data System organizations, plans, processes, and technical configurations, which were developed and employed to facilitate achievement of mission objectives, are described. In the Deep Space Network position of the tracking and data system, a number of special actions were taken to greatly increase the scientific data return and to assist the project in coping with in-flight problems. The benefits of such actions were high; however, there was also a significant increase in risk as a function of the experimental equipment and procedures required.

Neuro-Analogical Gate Tuning of Trajectory Data Fusion for a Mecanum-Wheeled Special Needs Chair

PubMed Central

ElSaharty, M. A.; zakzouk, Ezz Eldin

2017-01-01

Trajectory tracking of mobile wheeled chairs using internal shaft encoder and inertia measurement unit(IMU), exhibits several complications and accumulated errors in the tracking process due to wheel slippage, offset drift and integration approximations. These errors can be realized when comparing localization results from such sensors with a camera tracking system. In long trajectory tracking, such errors can accumulate and result in significant deviations which make data from these sensors unreliable for tracking. Meanwhile the utilization of an external camera tracking system is not always a feasible solution depending on the implementation environment. This paper presents a novel sensor fusion method that combines the measurements of internal sensors to accurately predict the location of the wheeled chair in an environment. The method introduces a new analogical OR gate structured with tuned parameters using multi-layer feedforward neural network denoted as “Neuro-Analogical Gate” (NAG). The resulting system minimize any deviation error caused by the sensors, thus accurately tracking the wheeled chair location without the requirement of an external camera tracking system. The fusion methodology has been tested with a prototype Mecanum wheel-based chair, and significant improvement over tracking response, error and performance has been observed. PMID:28045973

Etracker: A Mobile Gaze-Tracking System with Near-Eye Display Based on a Combined Gaze-Tracking Algorithm.

PubMed

Li, Bin; Fu, Hong; Wen, Desheng; Lo, WaiLun

2018-05-19

Eye tracking technology has become increasingly important for psychological analysis, medical diagnosis, driver assistance systems, and many other applications. Various gaze-tracking models have been established by previous researchers. However, there is currently no near-eye display system with accurate gaze-tracking performance and a convenient user experience. In this paper, we constructed a complete prototype of the mobile gaze-tracking system ' Etracker ' with a near-eye viewing device for human gaze tracking. We proposed a combined gaze-tracking algorithm. In this algorithm, the convolutional neural network is used to remove blinking images and predict coarse gaze position, and then a geometric model is defined for accurate human gaze tracking. Moreover, we proposed using the mean value of gazes to resolve pupil center changes caused by nystagmus in calibration algorithms, so that an individual user only needs to calibrate it the first time, which makes our system more convenient. The experiments on gaze data from 26 participants show that the eye center detection accuracy is 98% and Etracker can provide an average gaze accuracy of 0.53° at a rate of 30⁻60 Hz.

The wireless networking system of Earthquake precursor mobile field observation

NASA Astrophysics Data System (ADS)

Wang, C.; Teng, Y.; Wang, X.; Fan, X.; Wang, X.

2012-12-01

The mobile field observation network could be real-time, reliably record and transmit large amounts of data, strengthen the physical signal observations in specific regions and specific period, it can improve the monitoring capacity and abnormal tracking capability. According to the features of scatter everywhere, a large number of current earthquake precursor observation measuring points, networking technology is based on wireless broadband accessing McWILL system, the communication system of earthquake precursor mobile field observation would real-time, reliably transmit large amounts of data to the monitoring center from measuring points through the connection about equipment and wireless accessing system, broadband wireless access system and precursor mobile observation management center system, thereby implementing remote instrument monitoring and data transmition. At present, the earthquake precursor field mobile observation network technology has been applied to fluxgate magnetometer array geomagnetic observations of Tianzhu, Xichang,and Xinjiang, it can be real-time monitoring the working status of the observational instruments of large area laid after the last two or three years, large scale field operation. Therefore, it can get geomagnetic field data of the local refinement regions and provide high-quality observational data for impending earthquake tracking forecast. Although, wireless networking technology is very suitable for mobile field observation with the features of simple, flexible networking etc, it also has the phenomenon of packet loss etc when transmitting a large number of observational data due to the wireless relatively weak signal and narrow bandwidth. In view of high sampling rate instruments, this project uses data compression and effectively solves the problem of data transmission packet loss; Control commands, status data and observational data transmission use different priorities and means, which control the packet loss rate within an acceptable range and do not affect real-time observation curve. After field running test and earthquake tracking project applications, the field mobile observation wireless networking system is operate normally, various function have good operability and show good performance, the quality of data transmission meet the system design requirements and play a significant role in practical applications.

Time synchronization of NASA tracking stations via LORAN-C

NASA Technical Reports Server (NTRS)

Mazur, W. E., Jr.

1973-01-01

A report is presented of the results observed in comparison between LORAN-C and accurate portable clocks carried to the stations of NASA's world-wide space tracking and data network. It is believed that such information can provide a meaningful determination of the accuracy of the LORAN-C technique. The investigation shows the need for the employment of portable clocks during, or shortly after the installation of LORAN-C receivers.

A Distributed Multiobject Tracking Algorithm for Passive Sensor Networks

DTIC Science & Technology

1980-06-23

between the true acoustic azimuth and the filter estimate was computed for e4eii track. An overall aso was computed for each filter. The results of...h B sindB’ =°S6B (5.26) (Remember 6. is a negative quantity in this figure). Also, 1. tA- t = A (5.28)A t -t (5.29) From (5.25) and (5.26) we geL

Analysis of Spatial Autocorrelation for Optimal Observation Network in Korea

NASA Astrophysics Data System (ADS)

Park, S.; Lee, S.; Lee, E.; Park, S. K.

2016-12-01

Many studies for improving prediction of high-impact weather have been implemented, such as THORPEX (The Observing System Research and Predictability Experiment), FASTEX (Fronts and Atlantic Storm-Track Experiment), NORPEX (North Pacific Experiment), WSR/NOAA (Winter Storm Reconnaissance), and DOTSTAR (Dropwindsonde Observations for Typhoon Surveillance near the TAiwan Region). One of most important objectives in these studies is to find effects of observation on forecast, and to establish optimal observation network. However, there are lack of such studies on Korea, although Korean peninsula exhibits a highly complex terrain so it is difficult to predict its weather phenomena. Through building the future optimal observation network, it is necessary to increase utilization of numerical weather prediction and improve monitoring·tracking·prediction skills of high-impact weather in Korea. Therefore, we will perform preliminary study to understand the spatial scale for an expansion of observation system through Spatial Autocorrelation (SAC) analysis. In additions, we will develop a testbed system to design an optimal observation network. Analysis is conducted with Automatic Weather System (AWS) rainfall data, global upper air grid observation (i.e., temperature, pressure, humidity), Himawari satellite data (i.e., water vapor) during 2013-2015 of Korea. This study will provide a guideline to construct observation network for not only improving weather prediction skill but also cost-effectiveness.

Application of probabilistic fiber-tracking method of MR imaging to measure impact of cranial irradiation on structural brain connectivity in children treated for medulloblastoma

NASA Astrophysics Data System (ADS)

Duncan, Elizabeth C.; Reddick, Wilburn E.; Glass, John O.; Hyun, Jung Won; Ji, Qing; Li, Yimei; Gajjar, Amar

2016-03-01

We applied a modified probabilistic fiber-tracking method for the extraction of fiber pathways to quantify decreased white matter integrity as a surrogate of structural loss in connectivity due to cranial radiation therapy (CRT) as treatment for pediatric medulloblastoma. Thirty subjects were examined (n=8 average-risk, n=22 high-risk) and the groups did not differ significantly in age at examination. The pathway analysis created a structural connectome focused on sub-networks within the central executive network (CEN) for comparison between baseline and post-CRT scans and for comparison between standard and high dose CRT. A paired-wise comparison of the connectivity between baseline and post-CRT scans showed the irradiation did have a significant detrimental impact on white matter integrity (decreased fractional anisotropy (FA) and decreased axial diffusivity (AX)) in most of the CEN sub-networks. Group comparisons of the change in the connectivity revealed that patients receiving high dose CRT experienced significant AX decreases in all sub-networks while the patients receiving standard dose CRT had relatively stable AX measures across time. This study on pediatric patients with medulloblastoma demonstrated the utility of this method to identify specific sub-networks within the developing brain affected by CRT.

Minimum requirements for predictive pore-network modeling of solute transport in micromodels

NASA Astrophysics Data System (ADS)

Mehmani, Yashar; Tchelepi, Hamdi A.

2017-10-01

Pore-scale models are now an integral part of analyzing fluid dynamics in porous materials (e.g., rocks, soils, fuel cells). Pore network models (PNM) are particularly attractive due to their computational efficiency. However, quantitative predictions with PNM have not always been successful. We focus on single-phase transport of a passive tracer under advection-dominated regimes and compare PNM with high-fidelity direct numerical simulations (DNS) for a range of micromodel heterogeneities. We identify the minimum requirements for predictive PNM of transport. They are: (a) flow-based network extraction, i.e., discretizing the pore space based on the underlying velocity field, (b) a Lagrangian (particle tracking) simulation framework, and (c) accurate transfer of particles from one pore throat to the next. We develop novel network extraction and particle tracking PNM methods that meet these requirements. Moreover, we show that certain established PNM practices in the literature can result in first-order errors in modeling advection-dominated transport. They include: all Eulerian PNMs, networks extracted based on geometric metrics only, and flux-based nodal transfer probabilities. Preliminary results for a 3D sphere pack are also presented. The simulation inputs for this work are made public to serve as a benchmark for the research community.

Subnanosecond GPS-based clock synchronization and precision deep-space tracking

NASA Technical Reports Server (NTRS)

Dunn, C. E.; Lichten, S. M.; Jefferson, D. C.; Border, J. S.

1992-01-01

Interferometric spacecraft tracking is accomplished by the Deep Space Network (DSN) by comparing the arrival time of electromagnetic spacecraft signals at ground antennas separated by baselines on the order of 8000 km. Clock synchronization errors within and between DSN stations directly impact the attainable tracking accuracy, with a 0.3-nsec error in clock synchronization resulting in an 11-nrad angular position error. This level of synchronization is currently achieved by observing a quasar which is angularly close to the spacecraft just after the spacecraft observations. By determining the differential arrival times of the random quasar signal at the stations, clock offsets and propagation delays within the atmosphere and within the DSN stations are calibrated. Recent developments in time transfer techniques may allow medium accuracy (50-100 nrad) spacecraft tracking without near-simultaneous quasar-based calibrations. Solutions are presented for a worldwide network of Global Positioning System (GPS) receivers in which the formal errors for DSN clock offset parameters are less than 0.5 nsec. Comparisons of clock rate offsets derived from GPS measurements and from very long baseline interferometry (VLBI), as well as the examination of clock closure, suggest that these formal errors are a realistic measure of GPS-based clock offset precision and accuracy. Incorporating GPS-based clock synchronization measurements into a spacecraft differential ranging system would allow tracking without near-simultaneous quasar observations. The impact on individual spacecraft navigation-error sources due to elimination of quasar-based calibrations is presented. System implementation, including calibration of station electronic delays, is discussed.

Tracking moving identities: after attending the right location, the identity does not come for free.

PubMed

Pinto, Yaïr; Scholte, H Steven; Lamme, V A F

2012-01-01

Although tracking identical moving objects has been studied since the 1980's, only recently the study into tracking moving objects with distinct identities has started (referred to as Multiple Identity Tracking, MIT). So far, only behavioral studies into MIT have been undertaken. These studies have left a fundamental question regarding MIT unanswered, is MIT a one-stage or a two-stage process? According to the one-stage model, after a location has been attended, the identity is released without effort. However, according to the two-stage model, there are two effortful stages in MIT, attending to a location, and attending to the identity of the object at that location. In the current study we investigated this question by measuring brain activity in response to tracking familiar and unfamiliar targets. Familiarity is known to automate effortful processes, so if attention to identify the object is needed, this should become easier. However, if no such attention is needed, familiarity can only affect other processes (such as memory for the target set). Our results revealed that on unfamiliar trials neural activity was higher in both attentional networks, and visual identification networks. These results suggest that familiarity in MIT automates attentional identification processes, thus suggesting that attentional identification is needed in MIT. This then would imply that MIT is essentially a two-stage process, since after attending the location, the identity does not seem to come for free.

A Method for Using Player Tracking Data in Basketball to Learn Player Skills and Predict Team Performance.

PubMed

Skinner, Brian; Guy, Stephen J

2015-01-01

Player tracking data represents a revolutionary new data source for basketball analysis, in which essentially every aspect of a player's performance is tracked and can be analyzed numerically. We suggest a way by which this data set, when coupled with a network-style model of the offense that relates players' skills to the team's success at running different plays, can be used to automatically learn players' skills and predict the performance of untested 5-man lineups in a way that accounts for the interaction between players' respective skill sets. After developing a general analysis procedure, we present as an example a specific implementation of our method using a simplified network model. While player tracking data is not yet available in the public domain, we evaluate our model using simulated data and show that player skills can be accurately inferred by a simple statistical inference scheme. Finally, we use the model to analyze games from the 2011 playoff series between the Memphis Grizzlies and the Oklahoma City Thunder and we show that, even with a very limited data set, the model can consistently describe a player's interactions with a given lineup based only on his performance with a different lineup.

A Method for Using Player Tracking Data in Basketball to Learn Player Skills and Predict Team Performance

PubMed Central

Skinner, Brian; Guy, Stephen J.

2015-01-01

Player tracking data represents a revolutionary new data source for basketball analysis, in which essentially every aspect of a player’s performance is tracked and can be analyzed numerically. We suggest a way by which this data set, when coupled with a network-style model of the offense that relates players’ skills to the team’s success at running different plays, can be used to automatically learn players’ skills and predict the performance of untested 5-man lineups in a way that accounts for the interaction between players’ respective skill sets. After developing a general analysis procedure, we present as an example a specific implementation of our method using a simplified network model. While player tracking data is not yet available in the public domain, we evaluate our model using simulated data and show that player skills can be accurately inferred by a simple statistical inference scheme. Finally, we use the model to analyze games from the 2011 playoff series between the Memphis Grizzlies and the Oklahoma City Thunder and we show that, even with a very limited data set, the model can consistently describe a player’s interactions with a given lineup based only on his performance with a different lineup. PMID:26351846

Three-Dimensional Stereoscopic Tracking Velocimetry and Experimental/Numerical Comparison of Directional Solidification

NASA Technical Reports Server (NTRS)

Lee, David; Ge, Yi; Cha, Soyoung Stephen; Ramachandran, Narayanan; Rose, M. Franklin (Technical Monitor)

2001-01-01

Measurement of three-dimensional (3-D) three-component velocity fields is of great importance in both ground and space experiments for understanding materials processing and fluid physics. The experiments in these fields most likely inhibit the application of conventional planar probes for observing 3-D phenomena. Here, we present the investigation results of stereoscopic tracking velocimetry (STV) for measuring 3-D velocity fields, which include diagnostic technology development, experimental velocity measurement, and comparison with analytical and numerical computation. STV is advantageous in system simplicity for building compact hardware and in software efficiency for continual near-real-time monitoring. It has great freedom in illuminating and observing volumetric fields from arbitrary directions. STV is based on stereoscopic observation of particles-Seeded in a flow by CCD sensors. In the approach, part of the individual particle images that provide data points is likely to be lost or cause errors when their images overlap and crisscross each other especially under a high particle density. In order to maximize the valid recovery of data points, neural networks are implemented for these two important processes. For the step of particle overlap decomposition, the back propagation neural network is utilized because of its ability in pattern recognition with pertinent particle image feature parameters. For the step of particle tracking, the Hopfield neural network is employed to find appropriate particle tracks based on global optimization. Our investigation indicates that the neural networks are very efficient and useful for stereoscopically tracking particles. As an initial assessment of the diagnostic technology performance, laminar water jets with and without pulsation are measured. The jet tip velocity profiles are in good agreement with analytical predictions. Finally, for testing in material processing applications, a simple directional solidification apparatus is built for experimenting with a metal analog of succinonitrile. Its 3-D velocity field at the liquid phase is then measured to be compared with those from numerical computation. Our theoretical, numerical, and experimental investigations have proven STV to be a viable candidate for reliably measuring 3-D flow velocities. With current activities are focused on further improving the processing efficiency, overall accuracy, and automation, the eventual efforts of broad experimental applications and concurrent numerical modeling validation will be vital to many areas in fluid flow and materials processing.

Detecting brain dynamics during resting state: a tensor based evolutionary clustering approach

NASA Astrophysics Data System (ADS)

Al-sharoa, Esraa; Al-khassaweneh, Mahmood; Aviyente, Selin

2017-08-01

Human brain is a complex network with connections across different regions. Understanding the functional connectivity (FC) of the brain is important both during resting state and task; as disruptions in connectivity patterns are indicators of different psychopathological and neurological diseases. In this work, we study the resting state functional connectivity networks (FCNs) of the brain from fMRI BOLD signals. Recent studies have shown that FCNs are dynamic even during resting state and understanding the temporal dynamics of FCNs is important for differentiating between different conditions. Therefore, it is important to develop algorithms to track the dynamic formation and dissociation of FCNs of the brain during resting state. In this paper, we propose a two step tensor based community detection algorithm to identify and track the brain network community structure across time. First, we introduce an information-theoretic function to reduce the dynamic FCN and identify the time points that are similar topologically to combine them into a tensor. These time points will be used to identify the different FC states. Second, a tensor based spectral clustering approach is developed to identify the community structure of the constructed tensors. The proposed algorithm applies Tucker decomposition to the constructed tensors and extract the orthogonal factor matrices along the connectivity mode to determine the common subspace within each FC state. The detected community structure is summarized and described as FC states. The results illustrate the dynamic structure of resting state networks (RSNs), including the default mode network, somatomotor network, subcortical network and visual network.

On the effect of networks of cycle-tracks on the risk of cycling. The case of Seville.

PubMed

Marqués, R; Hernández-Herrador, V

2017-05-01

We analyze the evolution of the risk of cycling in Seville before and after the implementation of a network of segregated cycle tracks in the city. Specifically, we study the evolution of the risk for cyclists of being involved in a collision with a motor vehicle, using data reported by the traffic police along the period 2000-2013, i.e. seven years before and after the network was built. A sudden drop of such risk was observed after the implementation of the network of bikeways. We study, through a multilinear regression analysis, the evolution of the risk by means of explanatory variables representing changes in the built environment, specifically the length of the bikeways and a stepwise jump variable taking the values 0/1 before/after the network was implemented. We found that this last variable has a high value as explanatory variable, even higher than the length of the network, thus suggesting that networking the bikeways has a substantial effect on cycling safety by itself and beyond the mere increase in the length of the bikeways. We also analyze safety in numbers through a non-linear regression analysis. Our results fully agree qualitatively and quantitatively with the results previously reported by Jacobsen (2003), thus providing an independent confirmation of Jacobsen's results. Finally, the mutual causal relationships between the increase in safety, the increase in the number of cyclists and the presence of the network of bikeways are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Energy optimization for a wind DFIG with flywheel energy storage

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamzaoui, Ihssen, E-mail: hamzaoui-ihssen2000@yahoo.fr; Laboratory of Instrumentation, Faculty of Electronics and Computer, University of Khemis Miliana, Ain Defla; Bouchafaa, Farid, E-mail: fbouchafa@gmail.com

2016-07-25

The type of distributed generation unit that is the subject of this paper relates to renewable energy sources, especially wind power. The wind generator used is based on a double fed induction Generator (DFIG). The stator of the DFIG is connected directly to the network and the rotor is connected to the network through the power converter with three levels. The objective of this work is to study the association a Flywheel Energy Storage System (FESS) in wind generator. This system is used to improve the quality of electricity provided by wind generator. It is composed of a flywheel; anmore » induction machine (IM) and a power electronic converter. A maximum power tracking technique « Maximum Power Point Tracking » (MPPT) and a strategy for controlling the pitch angle is presented. The model of the complete system is developed in Matlab/Simulink environment / to analyze the results from simulation the integration of wind chain to networks.« less

Successful twilight observations of eta-Aquarid shower in "Unified Churyumov Network"

NASA Astrophysics Data System (ADS)

Steklov, E. A.; Kruchynenko, V. G.; Steklov, A. F.; Vidmachenko, A. P.; Dashkiev, G. N.

2017-05-01

On March 29 2013, on the left bank of the Dnieper in Kiev, young amateur astronomers, in the evening twilight, observed almost simultaneous invasion of three large fragments of meteoroid. Then four images were obtained. It was proposed to create a "Club of Fireball tracks observers". As a result, in Kiev region a network of photo hunters on twilight and daytime tracks of dangerous invasions into the sky above us - was formed. This "Unified Churyumov Network" has been in operation for four years. From April 19 to May 28, we are actively observing a meteor shower of eta-Aquarids. The particles of this meteor shower are fragments of nucleus of the famous Halley comet. In May 10 at the same time four observers photographed very interesting trail of invasion from four points of Kiev. In the last few years, the authors have registered several hundred small and dozens of larger invasions in the sky over Kiev and Kiev region.

Integration of communications and tracking data processing simulation for space station

NASA Technical Reports Server (NTRS)

Lacovara, Robert C.

1987-01-01

A simplified model of the communications network for the Communications and Tracking Data Processing System (CTDP) was developed. It was simulated by use of programs running on several on-site computers. These programs communicate with one another by means of both local area networks and direct serial connections. The domain of the model and its simulation is from Orbital Replaceable Unit (ORU) interface to Data Management Systems (DMS). The simulation was designed to allow status queries from remote entities across the DMS networks to be propagated through the model to several simulated ORU's. The ORU response is then propagated back to the remote entity which originated the request. Response times at the various levels were investigated in a multi-tasking, multi-user operating system environment. Results indicate that the effective bandwidth of the system may be too low to support expected data volume requirements under conventional operating systems. Instead, some form of embedded process control program may be required on the node computers.

Dynamic communities in multichannel data: an application to the foreign exchange market during the 2007-2008 credit crisis.

PubMed

Fenn, Daniel J; Porter, Mason A; McDonald, Mark; Williams, Stacy; Johnson, Neil F; Jones, Nick S

2009-09-01

We study the cluster dynamics of multichannel (multivariate) time series by representing their correlations as time-dependent networks and investigating the evolution of network communities. We employ a node-centric approach that allows us to track the effects of the community evolution on the functional roles of individual nodes without having to track entire communities. As an example, we consider a foreign exchange market network in which each node represents an exchange rate and each edge represents a time-dependent correlation between the rates. We study the period 2005-2008, which includes the recent credit and liquidity crisis. Using community detection, we find that exchange rates that are strongly attached to their community are persistently grouped with the same set of rates, whereas exchange rates that are important for the transfer of information tend to be positioned on the edges of communities. Our analysis successfully uncovers major trading changes that occurred in the market during the credit crisis.

The Software Correlator of the Chinese VLBI Network

NASA Technical Reports Server (NTRS)

Zheng, Weimin; Quan, Ying; Shu, Fengchun; Chen, Zhong; Chen, Shanshan; Wang, Weihua; Wang, Guangli

2010-01-01

The software correlator of the Chinese VLBI Network (CVN) has played an irreplaceable role in the CVN routine data processing, e.g., in the Chinese lunar exploration project. This correlator will be upgraded to process geodetic and astronomical observation data. In the future, with several new stations joining the network, CVN will carry out crustal movement observations, quick UT1 measurements, astrophysical observations, and deep space exploration activities. For the geodetic or astronomical observations, we need a wide-band 10-station correlator. For spacecraft tracking, a realtime and highly reliable correlator is essential. To meet the scientific and navigation requirements of CVN, two parallel software correlators in the multiprocessor environments are under development. A high speed, 10-station prototype correlator using the mixed Pthreads and MPI (Massage Passing Interface) parallel algorithm on a computer cluster platform is being developed. Another real-time software correlator for spacecraft tracking adopts the thread-parallel technology, and it runs on the SMP (Symmetric Multiple Processor) servers. Both correlators have the characteristic of flexible structure and scalability.

Dynamic communities in multichannel data: An application to the foreign exchange market during the 2007-2008 credit crisis

NASA Astrophysics Data System (ADS)

Fenn, Daniel J.; Porter, Mason A.; McDonald, Mark; Williams, Stacy; Johnson, Neil F.; Jones, Nick S.

2009-09-01

We study the cluster dynamics of multichannel (multivariate) time series by representing their correlations as time-dependent networks and investigating the evolution of network communities. We employ a node-centric approach that allows us to track the effects of the community evolution on the functional roles of individual nodes without having to track entire communities. As an example, we consider a foreign exchange market network in which each node represents an exchange rate and each edge represents a time-dependent correlation between the rates. We study the period 2005-2008, which includes the recent credit and liquidity crisis. Using community detection, we find that exchange rates that are strongly attached to their community are persistently grouped with the same set of rates, whereas exchange rates that are important for the transfer of information tend to be positioned on the edges of communities. Our analysis successfully uncovers major trading changes that occurred in the market during the credit crisis.

Building Cross-Country Networks for Laboratory Capacity and Improvement.

PubMed

Schneidman, Miriam; Matu, Martin; Nkengasong, John; Githui, Willie; Kalyesubula-Kibuuka, Simeon; Silva, Kelly Araujo

2018-03-01

Laboratory networks are vital to well-functioning public health systems and disease control efforts. Cross-country laboratory networks play a critical role in supporting epidemiologic surveillance, accelerating disease outbreak response, and tracking drug resistance. The East Africa Public Health Laboratory Network was established to bolster diagnostic and disease surveillance capacity. The network supports the introduction of regional quality standards; facilitates the rollout and evaluation of new diagnostic tools; and serves as a platform for training, research, and knowledge sharing. Participating facilities benefitted from state-of-the art investments, capacity building, and mentorship; conducted multicountry research studies; and contributed to disease outbreak response. Copyright © 2017 Elsevier Inc. All rights reserved.

Robust recognition of handwritten numerals based on dual cooperative network

NASA Technical Reports Server (NTRS)

Lee, Sukhan; Choi, Yeongwoo

1992-01-01

An approach to robust recognition of handwritten numerals using two operating parallel networks is presented. The first network uses inputs in Cartesian coordinates, and the second network uses the same inputs transformed into polar coordinates. How the proposed approach realizes the robustness to local and global variations of input numerals by handling inputs both in Cartesian coordinates and in its transformed Polar coordinates is described. The required network structures and its learning scheme are discussed. Experimental results show that by tracking only a small number of distinctive features for each teaching numeral in each coordinate, the proposed system can provide robust recognition of handwritten numerals.

Relating microstructure to rheology of a bundled and cross-linked F-actin network in vitro

NASA Astrophysics Data System (ADS)

Shin, J. H.; Gardel, M. L.; Mahadevan, L.; Matsudaira, P.; Weitz, D. A.

2004-06-01

The organization of individual actin filaments into higher-order structures is controlled by actin-binding proteins (ABPs). Although the biological significance of the ABPs is well documented, little is known about how bundling and cross-linking quantitatively affect the microstructure and mechanical properties of actin networks. Here we quantify the effect of the ABP scruin on actin networks by using imaging techniques, cosedimentation assays, multiparticle tracking, and bulk rheology. We show how the structure of the actin network is modified as the scruin concentration is varied, and we correlate these structural changes to variations in the resultant network elasticity.

Weighted Optimization-Based Distributed Kalman Filter for Nonlinear Target Tracking in Collaborative Sensor Networks.

PubMed

Chen, Jie; Li, Jiahong; Yang, Shuanghua; Deng, Fang

2017-11-01

The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem in collaborative sensor networks. According to the adaptive Kalman filtering (KF) method, the nonlinearity and coupling can be regarded as the model noise covariance, and estimated by minimizing the innovation or residual errors of the states. However, the method requires large time window of data to achieve reliable covariance measurement, making it impractical for nonlinear systems which are rapidly changing. To deal with the problem, a weighted optimization-based distributed KF algorithm (WODKF) is proposed in this paper. The algorithm enlarges the data size of each sensor by the received measurements and state estimates from its connected sensors instead of the time window. A new cost function is set as the weighted sum of the bias and oscillation of the state to estimate the "best" estimate of the model noise covariance. The bias and oscillation of the state of each sensor are estimated by polynomial fitting a time window of state estimates and measurements of the sensor and its neighbors weighted by the measurement noise covariance. The best estimate of the model noise covariance is computed by minimizing the weighted cost function using the exhaustive method. The sensor selection method is in addition to the algorithm to decrease the computation load of the filter and increase the scalability of the sensor network. The existence, suboptimality and stability analysis of the algorithm are given. The local probability data association method is used in the proposed algorithm for the multitarget tracking case. The algorithm is demonstrated in simulations on tracking examples for a random signal, one nonlinear target, and four nonlinear targets. Results show the feasibility and superiority of WODKF against other filtering algorithms for a large class of systems.

A low-cost mobile adaptive tracking system for chronic pulmonary patients in home environment.

PubMed

Işik, Ali Hakan; Güler, Inan; Sener, Melahat Uzel

2013-01-01

The main objective of this study is presenting a real-time mobile adaptive tracking system for patients diagnosed with diseases such as asthma or chronic obstructive pulmonary disease and application results at home. The main role of the system is to support and track chronic pulmonary patients in real time who are comfortable in their home environment. It is not intended to replace the doctor, regular treatment, and diagnosis. In this study, the Java 2 micro edition-based system is integrated with portable spirometry, smartphone, extensible markup language-based Web services, Web server, and Web pages for visualizing pulmonary function test results. The Bluetooth(®) (Bluetooth SIG, Kirkland, WA) virtual serial port protocol is used to obtain the test results from spirometry. General packet radio service, wireless local area network, or third-generation-based wireless networks are used to send the test results from a smartphone to the remote database. The system provides real-time classification of test results with the back propagation artificial neural network algorithm on a mobile smartphone. It also provides the generation of appropriate short message service-based notification and sending of all data to the Web server. In this study, the test results of 486 patients, obtained from Atatürk Chest Diseases and Thoracic Surgery Training and Research Hospital in Ankara, Turkey, are used as the training and test set in the algorithm. The algorithm has 98.7% accuracy, 97.83% specificity, 97.63% sensitivity, and 0.946 correlation values. The results show that the system is cheap (900 Euros) and reliable. The developed real-time system provides improvement in classification accuracy and facilitates tracking of chronic pulmonary patients.

iTAG: Integrating a Cloud Based, Collaborative Animal Tracking Network into the GCOOS data portal in the Gulf of Mexico

NASA Astrophysics Data System (ADS)

Kirkpatrick, B. A.; Currier, R. D.; Simoniello, C.

2016-02-01

The tagging and tracking of aquatic animals using acoustic telemetry hardware has traditionally been the purview of individual researchers that specialize in single species. Concerns over data privacy and unauthorized use of receiver arrays have prevented the construction of large-scale, multi-species, multi-institution, multi-researcher collaborative acoustic arrays. We have developed a toolset to build the new portal using the Flask microframework, Python language, and Twitter bootstrap. Initial feedback has been overwhelmingly positive. The privacy policy has been praised for its granularity: principal investigators can choose between three levels of privacy for all data and hardware: Completely private - viewable only by the PI Visible to iTAG members Visible to the general public At the time of this writing iTAG is still in the beta stage, but the feedback received to date indicates that with the proper design and security features, and an iterative cycle of feedback from potential members, constructing a collaborative acoustic tracking network system is possible. Initial usage will be limited to the entry and searching for `orphan/mystery' tags, with the integration of historical array deployments and data following shortly thereafter. We have also been working with staff from the Ocean Tracking Network to allow for integration of the two systems. The database schema of iTAG is based on the marine metadata convention for acoustic telemetry. This should permit machine-to-machine data exchange between iTAG and OTN. The integration of animal telemetry data into the GCOOS portal will allow researchers to easily access the physiochemical oceanography data, thus allowing for a more in depth understanding of animal response and usage patterns.

Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition.

PubMed

Wong, Sebastien C; Stamatescu, Victor; Gatt, Adam; Kearney, David; Lee, Ivan; McDonnell, Mark D

2017-10-01

This paper addresses the problem of online tracking and classification of multiple objects in an image sequence. Our proposed solution is to first track all objects in the scene without relying on object-specific prior knowledge, which in other systems can take the form of hand-crafted features or user-based track initialization. We then classify the tracked objects with a fast-learning image classifier, that is based on a shallow convolutional neural network architecture and demonstrate that object recognition improves when this is combined with object state information from the tracking algorithm. We argue that by transferring the use of prior knowledge from the detection and tracking stages to the classification stage, we can design a robust, general purpose object recognition system with the ability to detect and track a variety of object types. We describe our biologically inspired implementation, which adaptively learns the shape and motion of tracked objects, and apply it to the Neovision2 Tower benchmark data set, which contains multiple object types. An experimental evaluation demonstrates that our approach is competitive with the state-of-the-art video object recognition systems that do make use of object-specific prior knowledge in detection and tracking, while providing additional practical advantages by virtue of its generality.

Real-time multiple objects tracking on Raspberry-Pi-based smart embedded camera

NASA Astrophysics Data System (ADS)

Dziri, Aziz; Duranton, Marc; Chapuis, Roland

2016-07-01

Multiple-object tracking constitutes a major step in several computer vision applications, such as surveillance, advanced driver assistance systems, and automatic traffic monitoring. Because of the number of cameras used to cover a large area, these applications are constrained by the cost of each node, the power consumption, the robustness of the tracking, the processing time, and the ease of deployment of the system. To meet these challenges, the use of low-power and low-cost embedded vision platforms to achieve reliable tracking becomes essential in networks of cameras. We propose a tracking pipeline that is designed for fixed smart cameras and which can handle occlusions between objects. We show that the proposed pipeline reaches real-time processing on a low-cost embedded smart camera composed of a Raspberry-Pi board and a RaspiCam camera. The tracking quality and the processing speed obtained with the proposed pipeline are evaluated on publicly available datasets and compared to the state-of-the-art methods.

Stream Tracker: Crowd sourcing and remote sensing to monitor stream flow intermittence

NASA Astrophysics Data System (ADS)

Puntenney, K.; Kampf, S. K.; Newman, G.; Lefsky, M. A.; Weber, R.; Gerlich, J.

2017-12-01

Streams that do not flow continuously in time and space support diverse aquatic life and can be critical contributors to downstream water supply. However, these intermittent streams are rarely monitored and poorly mapped. Stream Tracker is a community powered stream monitoring project that pairs citizen contributed observations of streamflow presence or absence with a network of streamflow sensors and remotely sensed data from satellites to track when and where water is flowing in intermittent stream channels. Citizens can visit sites on roads and trails to track flow and contribute their observations to the project site hosted by CitSci.org. Data can be entered using either a mobile application with offline capabilities or an online data entry portal. The sensor network provides a consistent record of streamflow and flow presence/absence across a range of elevations and drainage areas. Capacitance, resistance, and laser sensors have been deployed to determine the most reliable, low cost sensor that could be mass distributed to track streamflow intermittence over a larger number of sites. Streamflow presence or absence observations from the citizen and sensor networks are then compared to satellite imagery to improve flow detection algorithms using remotely sensed data from Landsat. In the first two months of this project, 1,287 observations have been made at 241 sites by 24 project members across northern and western Colorado.