A Distributed Computing Network for Real-Time Systems

DTIC Science & Technology

1980-11-03

NUSC Tttchnical Docum&nt 5932 3 November 1980 A Distributed Computing N ~etwork for Real ·- Time Systems Gordon · E. Morrison Combat Control...megabit, 10 megabit, and 20 megabit networks. These values are well within the J state-of-the-art and are typical for real - time systems similar to

FPGA Based "Intelligent Tap" Device for Real-Time Ethernet Network Monitoring

NASA Astrophysics Data System (ADS)

Cupek, Rafał; Piękoś, Piotr; Poczobutt, Marcin; Ziębiński, Adam

This paper describes an "Intelligent Tap" - hardware device dedicated to support real-time Ethernet networks monitoring. Presented solution was created as a student project realized in Institute of Informatics, Silesian University of Technology with support from Softing A.G company. Authors provide description of realized FPGA based "Intelligent Tap" architecture dedicated for Real-Time Ethernet network monitoring systems. The practical device realization and feasibility study conclusions are presented also.

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

Fuzzy Logic-Based Guaranteed Lifetime Protocol for Real-Time Wireless Sensor Networks

PubMed Central

Shah, Babar; Iqbal, Farkhund; Abbas, Ali; Kim, Ki-Il

2015-01-01

Few techniques for guaranteeing a network lifetime have been proposed despite its great impact on network management. Moreover, since the existing schemes are mostly dependent on the combination of disparate parameters, they do not provide additional services, such as real-time communications and balanced energy consumption among sensor nodes; thus, the adaptability problems remain unresolved among nodes in wireless sensor networks (WSNs). To solve these problems, we propose a novel fuzzy logic model to provide real-time communication in a guaranteed WSN lifetime. The proposed fuzzy logic controller accepts the input descriptors energy, time and velocity to determine each node’s role for the next duration and the next hop relay node for real-time packets. Through the simulation results, we verified that both the guaranteed network’s lifetime and real-time delivery are efficiently ensured by the new fuzzy logic model. In more detail, the above-mentioned two performance metrics are improved up to 8%, as compared to our previous work, and 14% compared to existing schemes, respectively. PMID:26295238

Real-time hierarchically distributed processing network interaction simulation

NASA Technical Reports Server (NTRS)

Zimmerman, W. F.; Wu, C.

1987-01-01

The Telerobot Testbed is a hierarchically distributed processing system which is linked together through a standard, commercial Ethernet. Standard Ethernet systems are primarily designed to manage non-real-time information transfer. Therefore, collisions on the net (i.e., two or more sources attempting to send data at the same time) are managed by randomly rescheduling one of the sources to retransmit at a later time interval. Although acceptable for transmitting noncritical data such as mail, this particular feature is unacceptable for real-time hierarchical command and control systems such as the Telerobot. Data transfer and scheduling simulations, such as token ring, offer solutions to collision management, but do not appropriately characterize real-time data transfer/interactions for robotic systems. Therefore, models like these do not provide a viable simulation environment for understanding real-time network loading. A real-time network loading model is being developed which allows processor-to-processor interactions to be simulated, collisions (and respective probabilities) to be logged, collision-prone areas to be identified, and network control variable adjustments to be reentered as a means of examining and reducing collision-prone regimes that occur in the process of simulating a complete task sequence.

Quality of Service for Real-Time Applications Over Next Generation Data Networks

NASA Technical Reports Server (NTRS)

Ivancic, William; Atiquzzaman, Mohammed; Bai, Haowei; Su, Hongjun; Jain, Raj; Duresi, Arjan; Goyal, Mukyl; Bharani, Venkata; Liu, Chunlei; Kota, Sastri

2001-01-01

This project, which started on January 1, 2000, was funded by NASA Glenn Research Center for duration of one year. The deliverables of the project included the following tasks: Study of QoS mapping between the edge and core networks envisioned in the Next Generation networks will provide us with the QoS guarantees that can be obtained from next generation networks. Buffer management techniques to provide strict guarantees to real-time end-to-end applications through preferential treatment to packets belonging to real-time applications. In particular, use of ECN to help reduce the loss on high bandwidth-delay product satellite networks needs to be studied. Effect of Prioritized Packet Discard to increase goodput of the network and reduce the buffering requirements in the ATM switches. Provision of new IP circuit emulation services over Satellite IP backbones using MPLS will be studied. Determine the architecture and requirements for internetworking ATN and the Next Generation Internet for real-time applications.

Fast packet switching algorithms for dynamic resource control over ATM networks

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

Tsang, R.P.; Keattihananant, P.; Chang, T.

1996-12-01

Real-time continuous media traffic, such as digital video and audio, is expected to comprise a large percentage of the network load on future high speed packet switch networks such as ATM. A major feature which distinguishes high speed networks from traditional slower speed networks is the large amount of data the network must process very quickly. For efficient network usage, traffic control mechanisms are essential. Currently, most mechanisms for traffic control (such as flow control) have centered on the support of Available Bit Rate (ABR), i.e., non real-time, traffic. With regard to ATM, for ABR traffic, two major types ofmore » schemes which have been proposed are rate- control and credit-control schemes. Neither of these schemes are directly applicable to Real-time Variable Bit Rate (VBR) traffic such as continuous media traffic. Traffic control for continuous media traffic is an inherently difficult problem due to the time- sensitive nature of the traffic and its unpredictable burstiness. In this study, we present a scheme which controls traffic by dynamically allocating/de- allocating resources among competing VCs based upon their real-time requirements. This scheme incorporates a form of rate- control, real-time burst-level scheduling and link-link flow control. We show analytically potential performance improvements of our rate- control scheme and present a scheme for buffer dimensioning. We also present simulation results of our schemes and discuss the tradeoffs inherent in maintaining high network utilization and statistically guaranteeing many users` Quality of Service.« less

D-MSR: a distributed network management scheme for real-time monitoring and process control applications in wireless industrial automation.

PubMed

Zand, Pouria; Dilo, Arta; Havinga, Paul

2013-06-27

Current wireless technologies for industrial applications, such as WirelessHART and ISA100.11a, use a centralized management approach where a central network manager handles the requirements of the static network. However, such a centralized approach has several drawbacks. For example, it cannot cope with dynamicity/disturbance in large-scale networks in a real-time manner and it incurs a high communication overhead and latency for exchanging management traffic. In this paper, we therefore propose a distributed network management scheme, D-MSR. It enables the network devices to join the network, schedule their communications, establish end-to-end connections by reserving the communication resources for addressing real-time requirements, and cope with network dynamicity (e.g., node/edge failures) in a distributed manner. According to our knowledge, this is the first distributed management scheme based on IEEE 802.15.4e standard, which guides the nodes in different phases from joining until publishing their sensor data in the network. We demonstrate via simulation that D-MSR can address real-time and reliable communication as well as the high throughput requirements of industrial automation wireless networks, while also achieving higher efficiency in network management than WirelessHART, in terms of delay and overhead.

D-MSR: A Distributed Network Management Scheme for Real-Time Monitoring and Process Control Applications in Wireless Industrial Automation

PubMed Central

Zand, Pouria; Dilo, Arta; Havinga, Paul

2013-01-01

Current wireless technologies for industrial applications, such as WirelessHART and ISA100.11a, use a centralized management approach where a central network manager handles the requirements of the static network. However, such a centralized approach has several drawbacks. For example, it cannot cope with dynamicity/disturbance in large-scale networks in a real-time manner and it incurs a high communication overhead and latency for exchanging management traffic. In this paper, we therefore propose a distributed network management scheme, D-MSR. It enables the network devices to join the network, schedule their communications, establish end-to-end connections by reserving the communication resources for addressing real-time requirements, and cope with network dynamicity (e.g., node/edge failures) in a distributed manner. According to our knowledge, this is the first distributed management scheme based on IEEE 802.15.4e standard, which guides the nodes in different phases from joining until publishing their sensor data in the network. We demonstrate via simulation that D-MSR can address real-time and reliable communication as well as the high throughput requirements of industrial automation wireless networks, while also achieving higher efficiency in network management than WirelessHART, in terms of delay and overhead. PMID:23807687

ANZA Seismic Network- From Monitoring to Science

NASA Astrophysics Data System (ADS)

Vernon, F.; Eakin, J.; Martynov, V.; Newman, R.; Offield, G.; Hindley, A.; Astiz, L.

2007-05-01

The ANZA Seismic Network (http:eqinfo.ucsd.edu) utilizes broadband and strong motion sensors with 24-bit dataloggers combined with real-time telemetry to monitor local and regional seismicity in southernmost California. The ANZA network provides real-time data to the IRIS DMC, California Integrated Seismic Network (CISN), other regional networks, and the Advanced National Seismic System (ANSS), in addition to providing near real-time information and monitoring to the greater San Diego community. Twelve high dynamic range broadband and strong motion sensors adjacent to the San Jacinto Fault zone contribute data for earthquake source studies and continue the monitoring of the seismic activity of the San Jacinto fault initiated 24 years ago. Five additional stations are located in the San Diego region with one more station on San Clemente Island. The ANZA network uses the advance wireless networking capabilities of the NSF High Performance Wireless Research and Education Network (http:hpwren.ucsd.edu) to provide the communication infrastructure for the real-time telemetry of Anza seismic stations. The ANZA network uses the Antelope data acquisition software. The combination of high quality hardware, communications, and software allow for an annual network uptime in excess of 99.5% with a median annual station real-time data return rate of 99.3%. Approximately 90,000 events, dominantly local sources but including regional and teleseismic events, comprise the ANZA network waveform database. All waveform data and event data are managed using the Datascope relational database. The ANZA network data has been used in a variety of scientific research including detailed structure of the San Jacinto Fault Zone, earthquake source physics, spatial and temporal studies of aftershocks, array studies of teleseismic body waves, and array studies on the source of microseisms. To augment the location, detection, and high frequency observations of the seismic source spectrum from local earthquakes, the ANZA network is receiving real-time data from borehole arrays located at the UCSD Thornton Hospital, and from UCSB's Borrego Valley and Garner Valley Downhole Arrays. Finally the ANZA network is acquiring data from seven PBO sites each with 300 meter deep MEMs accelerometers, passive seismometers, and a borehole strainmeter.

VORBrouter: A dynamic data routing system for Real-Time Seismic networks

NASA Astrophysics Data System (ADS)

Hansen, T.; Vernon, F.; Lindquist, K.; Orcutt, J.

2004-12-01

For anyone who has managed a moderately complex buffered real-time data transport system, the need for reliable adaptive data transport is clear. The ROADNet VORBrouter system, an extension to the ROADNet data catalog system [AGU-2003, Dynamic Dataflow Topology Monitoring for Real-time Seismic Networks], allows dynamic routing of real-time seismic data from sensor to end-user. Traditional networks consist of a series of data buffer computers with data transport interconnections configured by hand. This allows for arbitrarily complex data networks, which can often exceed full comprehension by network administrators, sometimes resulting in data loops or accidental data cutoff. In order to manage data transport systems in the event of a network failure, a network administrator must be called upon to change the data transport paths and to recover the missing data. Using VORBrouter, administrators can sleep at night while still providing 7/24 uninterupted data streams at realistic cost. This software package uses information from the ROADNet data catalog system to route packets around failed link outages and to new consumers in real-time. Dynamic data routing protocols operating on top of the Antelope Data buffering layer allow authorized users to request data sets from their local buffer and to have them delivered from anywhere within the network of buffers. The VORBrouter software also allows for dynamic routing around network outages, and the elimination of duplicate data paths within the network, while maintaining the nearly lossless data transport features exhibited by the underlying Antelope system. We present the design of the VORBrouter system, its features, limitations and some future research directions.

Development of a Laboratory Synchrophasor Network and an Application to Estimate Transmission Line Parameters in Real Time

NASA Astrophysics Data System (ADS)

Almiron Bonnin, Rubens Eduardo

The development of an experimental synchrophasors network and application of synchrophasors for real-time transmission line parameter monitoring are presented in this thesis. In the laboratory setup, a power system is simulated in a RTDS real-time digital simulator, and the simulated voltages and currents are input to hardware phasor measurement units (PMUs) through the analog outputs of the simulator. Time synchronizing signals for the PMU devices are supplied from a common GPS clock. The real time data collected from PMUs are sent to a phasor data concentrator (PDC) through Ethernet using the TCP/IP protocol. A real-time transmission line parameter monitoring application program that uses the synchrophasor data provided by the PDC is implemented and validated. The experimental synchrophasor network developed in this thesis is expected to be used in research on synchrophasor applications as well as in graduate and undergraduate teaching.

Real-time video streaming in mobile cloud over heterogeneous wireless networks

NASA Astrophysics Data System (ADS)

Abdallah-Saleh, Saleh; Wang, Qi; Grecos, Christos

2012-06-01

Recently, the concept of Mobile Cloud Computing (MCC) has been proposed to offload the resource requirements in computational capabilities, storage and security from mobile devices into the cloud. Internet video applications such as real-time streaming are expected to be ubiquitously deployed and supported over the cloud for mobile users, who typically encounter a range of wireless networks of diverse radio access technologies during their roaming. However, real-time video streaming for mobile cloud users across heterogeneous wireless networks presents multiple challenges. The network-layer quality of service (QoS) provision to support high-quality mobile video delivery in this demanding scenario remains an open research question, and this in turn affects the application-level visual quality and impedes mobile users' perceived quality of experience (QoE). In this paper, we devise a framework to support real-time video streaming in this new mobile video networking paradigm and evaluate the performance of the proposed framework empirically through a lab-based yet realistic testing platform. One particular issue we focus on is the effect of users' mobility on the QoS of video streaming over the cloud. We design and implement a hybrid platform comprising of a test-bed and an emulator, on which our concept of mobile cloud computing, video streaming and heterogeneous wireless networks are implemented and integrated to allow the testing of our framework. As representative heterogeneous wireless networks, the popular WLAN (Wi-Fi) and MAN (WiMAX) networks are incorporated in order to evaluate effects of handovers between these different radio access technologies. The H.264/AVC (Advanced Video Coding) standard is employed for real-time video streaming from a server to mobile users (client nodes) in the networks. Mobility support is introduced to enable continuous streaming experience for a mobile user across the heterogeneous wireless network. Real-time video stream packets are captured for analytical purposes on the mobile user node. Experimental results are obtained and analysed. Future work is identified towards further improvement of the current design and implementation. With this new mobile video networking concept and paradigm implemented and evaluated, results and observations obtained from this study would form the basis of a more in-depth, comprehensive understanding of various challenges and opportunities in supporting high-quality real-time video streaming in mobile cloud over heterogeneous wireless networks.

Real-Time Optimization in Complex Stochastic Environment

DTIC Science & Technology

2015-06-24

simpler ones, thus addressing scalability and the limited resources of networked wireless devices. This, however, comes at the expense of increased...Maximization of Wireless Sensor Networks with Non-ideal Batteries”, IEEE Trans. on Control of Network Systems, Vol. 1, 1, pp. 86-98, 2014. [27...C.G., “Optimal Energy-Efficient Downlink Transmission Scheduling for Real-Time Wireless Networks ”, subm. to IEEE Trans. on Control of Network Systems

Using architecture information and real-time resource state to reduce power consumption and communication costs in parallel applications.

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

Brandt, James M.; Devine, Karen Dragon; Gentile, Ann C.

2014-09-01

As computer systems grow in both size and complexity, the need for applications and run-time systems to adjust to their dynamic environment also grows. The goal of the RAAMP LDRD was to combine static architecture information and real-time system state with algorithms to conserve power, reduce communication costs, and avoid network contention. We devel- oped new data collection and aggregation tools to extract static hardware information (e.g., node/core hierarchy, network routing) as well as real-time performance data (e.g., CPU uti- lization, power consumption, memory bandwidth saturation, percentage of used bandwidth, number of network stalls). We created application interfaces that allowedmore » this data to be used easily by algorithms. Finally, we demonstrated the benefit of integrating system and application information for two use cases. The first used real-time power consumption and memory bandwidth saturation data to throttle concurrency to save power without increasing application execution time. The second used static or real-time network traffic information to reduce or avoid network congestion by remapping MPI tasks to allocated processors. Results from our work are summarized in this report; more details are available in our publications [2, 6, 14, 16, 22, 29, 38, 44, 51, 54].« less

Efficient Probabilistic Diagnostics for Electrical Power Systems

NASA Technical Reports Server (NTRS)

Mengshoel, Ole J.; Chavira, Mark; Cascio, Keith; Poll, Scott; Darwiche, Adnan; Uckun, Serdar

2008-01-01

We consider in this work the probabilistic approach to model-based diagnosis when applied to electrical power systems (EPSs). Our probabilistic approach is formally well-founded, as it based on Bayesian networks and arithmetic circuits. We investigate the diagnostic task known as fault isolation, and pay special attention to meeting two of the main challenges . model development and real-time reasoning . often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-to-use speci.cation language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In essence, we introduce a high-level EPS speci.cation language from which Bayesian networks that can diagnose multiple simultaneous failures are auto-generated, and we illustrate the feasibility of using arithmetic circuits, compiled from Bayesian networks, for real-time diagnosis on real-world EPSs of interest to NASA. The experimental system is a real-world EPS, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. In experiments with the ADAPT Bayesian network, which currently contains 503 discrete nodes and 579 edges, we .nd high diagnostic accuracy in scenarios where one to three faults, both in components and sensors, were inserted. The time taken to compute the most probable explanation using arithmetic circuits has a small mean of 0.2625 milliseconds and standard deviation of 0.2028 milliseconds. In experiments with data from ADAPT we also show that arithmetic circuit evaluation substantially outperforms joint tree propagation and variable elimination, two alternative algorithms for diagnosis using Bayesian network inference.

ROADNET: A Real-time Data Aware System for Earth, Oceanographic, and Environmental Applications

NASA Astrophysics Data System (ADS)

Vernon, F.; Hansen, T.; Lindquist, K.; Ludascher, B.; Orcutt, J.; Rajasekar, A.

2003-12-01

The Real-time Observatories, Application, and Data management Network (ROADNet) Program aims to develop an integrated, seamless, and transparent environmental information network that will deliver geophysical, oceanographic, hydrological, ecological, and physical data to a variety of users in real-time. ROADNet is a multidisciplinary, multinational partnership of researchers, policymakers, natural resource managers, educators, and students who aim to use the data to advance our understanding and management of coastal, ocean, riparian, and terrestrial Earth systems in Southern California, Mexico, and well off shore. To date, project activity and funding have focused on the design and deployment of network linkages and on the exploratory development of the real-time data management system. We are currently adapting powerful "Data Grid" technologies to the unique challenges associated with the management and manipulation of real-time data. Current "Grid" projects deal with static data files, and significant technical innovation is required to address fundamental problems of real-time data processing, integration, and distribution. The technologies developed through this research will create a system that dynamically adapt downstream processing, cataloging, and data access interfaces when sensors are added or removed from the system; provide for real-time processing and monitoring of data streams--detecting events, and triggering computations, sensor and logger modifications, and other actions; integrate heterogeneous data from multiple (signal) domains; and provide for large-scale archival and querying of "consolidated" data. The software tools which must be developed do not exist, although limited prototype systems are available. This research has implications for the success of large-scale NSF initiatives in the Earth sciences (EarthScope), ocean sciences (OOI- Ocean Observatories Initiative), biological sciences (NEON - National Ecological Observatory Network) and civil engineering (NEES - Network for Earthquake Engineering Simulation). Each of these large scale initiatives aims to collect real-time data from thousands of sensors, and each will require new technologies to process, manage, and communicate real-time multidisciplinary environmental data on regional, national, and global scales.

Real-time logic modelling on SpaceWire

NASA Astrophysics Data System (ADS)

Zhou, Qiang; Ma, Yunpeng; Fei, Haidong; Wang, Xingyou

2017-04-01

A SpaceWire is a standard for on-board satellite networks as the basis for future data-handling architectures. However, it cannot meet the deterministic requirement for safety/time critical application in spacecraft, where the delay of real-time (RT) message streams must be guaranteed. Therefore, SpaceWire-D is developed that provides deterministic delivery over a SpaceWire network. Formal analysis and verification of real-time systems is critical to their development and safe implementation, and is a prerequisite for obtaining their safety certification. Failure to meet specified timing constraints such as deadlines in hard real-time systems may lead to catastrophic results. In this paper, a formal verification method, Real-Time Logic (RTL), has been proposed to specify and verify timing properties of SpaceWire-D network. Based on the principal of SpaceWire-D protocol, we firstly analyze the timing properties of fundamental transactions, such as RMAP WRITE, and RMAP READ. After that, the RMAP WRITE transaction structure is modeled in Real-Time Logic (RTL) and Presburger Arithmetic representations. And then, the associated constraint graph and safety analysis is provided. Finally, it is suggested that RTL method can be useful for the protocol evaluation and provision of recommendation for further protocol evolutions.

Real-time video streaming using H.264 scalable video coding (SVC) in multihomed mobile networks: a testbed approach

NASA Astrophysics Data System (ADS)

Nightingale, James; Wang, Qi; Grecos, Christos

2011-03-01

Users of the next generation wireless paradigm known as multihomed mobile networks expect satisfactory quality of service (QoS) when accessing streamed multimedia content. The recent H.264 Scalable Video Coding (SVC) extension to the Advanced Video Coding standard (AVC), offers the facility to adapt real-time video streams in response to the dynamic conditions of multiple network paths encountered in multihomed wireless mobile networks. Nevertheless, preexisting streaming algorithms were mainly proposed for AVC delivery over multipath wired networks and were evaluated by software simulation. This paper introduces a practical, hardware-based testbed upon which we implement and evaluate real-time H.264 SVC streaming algorithms in a realistic multihomed wireless mobile networks environment. We propose an optimised streaming algorithm with multi-fold technical contributions. Firstly, we extended the AVC packet prioritisation schemes to reflect the three-dimensional granularity of SVC. Secondly, we designed a mechanism for evaluating the effects of different streamer 'read ahead window' sizes on real-time performance. Thirdly, we took account of the previously unconsidered path switching and mobile networks tunnelling overheads encountered in real-world deployments. Finally, we implemented a path condition monitoring and reporting scheme to facilitate the intelligent path switching. The proposed system has been experimentally shown to offer a significant improvement in PSNR of the received stream compared with representative existing algorithms.

Deep architecture neural network-based real-time image processing for image-guided radiotherapy.

PubMed

Mori, Shinichiro

2017-08-01

To develop real-time image processing for image-guided radiotherapy, we evaluated several neural network models for use with different imaging modalities, including X-ray fluoroscopic image denoising. Setup images of prostate cancer patients were acquired with two oblique X-ray fluoroscopic units. Two types of residual network were designed: a convolutional autoencoder (rCAE) and a convolutional neural network (rCNN). We changed the convolutional kernel size and number of convolutional layers for both networks, and the number of pooling and upsampling layers for rCAE. The ground-truth image was applied to the contrast-limited adaptive histogram equalization (CLAHE) method of image processing. Network models were trained to keep the quality of the output image close to that of the ground-truth image from the input image without image processing. For image denoising evaluation, noisy input images were used for the training. More than 6 convolutional layers with convolutional kernels >5×5 improved image quality. However, this did not allow real-time imaging. After applying a pair of pooling and upsampling layers to both networks, rCAEs with >3 convolutions each and rCNNs with >12 convolutions with a pair of pooling and upsampling layers achieved real-time processing at 30 frames per second (fps) with acceptable image quality. Use of our suggested network achieved real-time image processing for contrast enhancement and image denoising by the use of a conventional modern personal computer. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Agent-based real-time signal coordination in congested networks.

DOT National Transportation Integrated Search

2014-01-01

This study is the continuation of a previous NEXTRANS study on agent-based reinforcement : learning methods for signal coordination in congested networks. In the previous study, the : formulation of a real-time agent-based traffic signal control in o...

Real-time visual communication to aid disaster recovery in a multi-segment hybrid wireless networking system

NASA Astrophysics Data System (ADS)

Al Hadhrami, Tawfik; Wang, Qi; Grecos, Christos

2012-06-01

When natural disasters or other large-scale incidents occur, obtaining accurate and timely information on the developing situation is vital to effective disaster recovery operations. High-quality video streams and high-resolution images, if available in real time, would provide an invaluable source of current situation reports to the incident management team. Meanwhile, a disaster often causes significant damage to the communications infrastructure. Therefore, another essential requirement for disaster management is the ability to rapidly deploy a flexible incident area communication network. Such a network would facilitate the transmission of real-time video streams and still images from the disrupted area to remote command and control locations. In this paper, a comprehensive end-to-end video/image transmission system between an incident area and a remote control centre is proposed and implemented, and its performance is experimentally investigated. In this study a hybrid multi-segment communication network is designed that seamlessly integrates terrestrial wireless mesh networks (WMNs), distributed wireless visual sensor networks, an airborne platform with video camera balloons, and a Digital Video Broadcasting- Satellite (DVB-S) system. By carefully integrating all of these rapidly deployable, interworking and collaborative networking technologies, we can fully exploit the joint benefits provided by WMNs, WSNs, balloon camera networks and DVB-S for real-time video streaming and image delivery in emergency situations among the disaster hit area, the remote control centre and the rescue teams in the field. The whole proposed system is implemented in a proven simulator. Through extensive simulations, the real-time visual communication performance of this integrated system has been numerically evaluated, towards a more in-depth understanding in supporting high-quality visual communications in such a demanding context.

A Study of Quality of Service Communication for High-Speed Packet-Switching Computer Sub-Networks

NASA Technical Reports Server (NTRS)

Cui, Zhenqian

1999-01-01

With the development of high-speed networking technology, computer networks, including local-area networks (LANs), wide-area networks (WANs) and the Internet, are extending their traditional roles of carrying computer data. They are being used for Internet telephony, multimedia applications such as conferencing and video on demand, distributed simulations, and other real-time applications. LANs are even used for distributed real-time process control and computing as a cost-effective approach. Differing from traditional data transfer, these new classes of high-speed network applications (video, audio, real-time process control, and others) are delay sensitive. The usefulness of data depends not only on the correctness of received data, but also the time that data are received. In other words, these new classes of applications require networks to provide guaranteed services or quality of service (QoS). Quality of service can be defined by a set of parameters and reflects a user's expectation about the underlying network's behavior. Traditionally, distinct services are provided by different kinds of networks. Voice services are provided by telephone networks, video services are provided by cable networks, and data transfer services are provided by computer networks. A single network providing different services is called an integrated-services network.

Evaluation of Earthquake Detection Performance in Terms of Quality and Speed in SEISCOMP3 Using New Modules Qceval, Npeval and Sceval

NASA Astrophysics Data System (ADS)

Roessler, D.; Weber, B.; Ellguth, E.; Spazier, J.

2017-12-01

The geometry of seismic monitoring networks, site conditions and data availability as well as monitoring targets and strategies typically impose trade-offs between data quality, earthquake detection sensitivity, false detections and alert times. Network detection capabilities typically change with alteration of the seismic noise level by human activity or by varying weather and sea conditions. To give helpful information to operators and maintenance coordinators, gempa developed a range of tools to evaluate earthquake detection and network performance including qceval, npeval and sceval. qceval is a module which analyzes waveform quality parameters in real-time and deactivates and reactivates data streams based on waveform quality thresholds for automatic processing. For example, thresholds can be defined for latency, delay, timing quality, spikes and gaps count and rms. As changes in the automatic processing have a direct influence on detection quality and speed, another tool called "npeval" was designed to calculate in real-time the expected time needed to detect and locate earthquakes by evaluating the effective network geometry. The effective network geometry is derived from the configuration of stations participating in the detection. The detection times are shown as an additional layer on the map and updated in real-time as soon as the effective network geometry changes. Yet another new tool, "sceval", is an automatic module which classifies located seismic events (Origins) in real-time. sceval evaluates the spatial distribution of the stations contributing to an Origin. It confirms or rejects the status of Origins, adds comments or leaves the Origin unclassified. The comments are passed to an additional sceval plug-in where the end user can customize event types. This unique identification of real and fake events in earthquake catalogues allows to lower network detection thresholds. In real-time monitoring situations operators can limit the processing to events with unclassified Origins, reducing their workload. Classified Origins can be treated specifically by other procedures. These modules have been calibrated and fully tested by several complex seismic monitoring networks in the region of Indonesia and Northern Chile.

Direct modulation of aberrant brain network connectivity through real-time NeuroFeedback.

PubMed

Ramot, Michal; Kimmich, Sara; Gonzalez-Castillo, Javier; Roopchansingh, Vinai; Popal, Haroon; White, Emily; Gotts, Stephen J; Martin, Alex

2017-09-16

The existence of abnormal connectivity patterns between resting state networks in neuropsychiatric disorders, including Autism Spectrum Disorder (ASD), has been well established. Traditional treatment methods in ASD are limited, and do not address the aberrant network structure. Using real-time fMRI neurofeedback, we directly trained three brain nodes in participants with ASD, in which the aberrant connectivity has been shown to correlate with symptom severity. Desired network connectivity patterns were reinforced in real-time, without participants' awareness of the training taking place. This training regimen produced large, significant long-term changes in correlations at the network level, and whole brain analysis revealed that the greatest changes were focused on the areas being trained. These changes were not found in the control group. Moreover, changes in ASD resting state connectivity following the training were correlated to changes in behavior, suggesting that neurofeedback can be used to directly alter complex, clinically relevant network connectivity patterns.

REAL TIME CONTROL OF URBAN DRAINAGE NETWORKS

EPA Science Inventory

Real-time control (RTC) is a custom-designed, computer-assisted management technology for a specific sewerage network to meet the operational objectives of its collection/conveyance system. RTC can operate in several modes, including a mode that is activated during a wet weather ...

Adaptive route choice modeling in uncertain traffic networks with real-time information.

DOT National Transportation Integrated Search

2013-03-01

The objective of the research is to study travelers' route choice behavior in uncertain traffic networks : with real-time information. The research is motivated by two observations of the traffic system: 1) : the system is inherently uncertain with r...

Southern California Seismic Network: New Design and Implementation of Redundant and Reliable Real-time Data Acquisition Systems

NASA Astrophysics Data System (ADS)

Saleh, T.; Rico, H.; Solanki, K.; Hauksson, E.; Friberg, P.

2005-12-01

The Southern California Seismic Network (SCSN) handles more than 2500 high-data rate channels from more than 380 seismic stations distributed across southern California. These data are imported real-time from dataloggers, earthworm hubs, and partner networks. The SCSN also exports data to eight different partner networks. Both the imported and exported data are critical for emergency response and scientific research. Previous data acquisition systems were complex and difficult to operate, because they grew in an ad hoc fashion to meet the increasing needs for distributing real-time waveform data. To maximize reliability and redundancy, we apply best practices methods from computer science for implementing the software and hardware configurations for import, export, and acquisition of real-time seismic data. Our approach makes use of failover software designs, methods for dividing labor diligently amongst the network nodes, and state of the art networking redundancy technologies. To facilitate maintenance and daily operations we seek to provide some separation between major functions such as data import, export, acquisition, archiving, real-time processing, and alarming. As an example, we make waveform import and export functions independent by operating them on separate servers. Similarly, two independent servers provide waveform export, allowing data recipients to implement their own redundancy. The data import is handled differently by using one primary server and a live backup server. These data import servers, run fail-over software that allows automatic role switching in case of failure from primary to shadow. Similar to the classic earthworm design, all the acquired waveform data are broadcast onto a private network, which allows multiple machines to acquire and process the data. As we separate data import and export away from acquisition, we are also working on new approaches to separate real-time processing and rapid reliable archiving of real-time data. Further, improved network security is an integral part of the new design. Redundant firewalls will provide secure data imports, exports, and acquisition as well as DMZ zones for web servers and other publicly available servers. We will present the detailed design of this new configuration that is currently being implemented by the SCSN at Caltech. The design principals are general enough to be of use to most regional seismic networks.

Real-Time QoS Routing Protocols in Wireless Multimedia Sensor Networks: Study and Analysis.

PubMed

Alanazi, Adwan; Elleithy, Khaled

2015-09-02

Many routing protocols have been proposed for wireless sensor networks. These routing protocols are almost always based on energy efficiency. However, recent advances in complementary metal-oxide semiconductor (CMOS) cameras and small microphones have led to the development of Wireless Multimedia Sensor Networks (WMSN) as a class of wireless sensor networks which pose additional challenges. The transmission of imaging and video data needs routing protocols with both energy efficiency and Quality of Service (QoS) characteristics in order to guarantee the efficient use of the sensor nodes and effective access to the collected data. Also, with integration of real time applications in Wireless Senor Networks (WSNs), the use of QoS routing protocols is not only becoming a significant topic, but is also gaining the attention of researchers. In designing an efficient QoS routing protocol, the reliability and guarantee of end-to-end delay are critical events while conserving energy. Thus, considerable research has been focused on designing energy efficient and robust QoS routing protocols. In this paper, we present a state of the art research work based on real-time QoS routing protocols for WMSNs that have already been proposed. This paper categorizes the real-time QoS routing protocols into probabilistic and deterministic protocols. In addition, both categories are classified into soft and hard real time protocols by highlighting the QoS issues including the limitations and features of each protocol. Furthermore, we have compared the performance of mobility-aware query based real-time QoS routing protocols from each category using Network Simulator-2 (NS2). This paper also focuses on the design challenges and future research directions as well as highlights the characteristics of each QoS routing protocol.

Real-Time QoS Routing Protocols in Wireless Multimedia Sensor Networks: Study and Analysis

PubMed Central

Alanazi, Adwan; Elleithy, Khaled

2015-01-01

Many routing protocols have been proposed for wireless sensor networks. These routing protocols are almost always based on energy efficiency. However, recent advances in complementary metal-oxide semiconductor (CMOS) cameras and small microphones have led to the development of Wireless Multimedia Sensor Networks (WMSN) as a class of wireless sensor networks which pose additional challenges. The transmission of imaging and video data needs routing protocols with both energy efficiency and Quality of Service (QoS) characteristics in order to guarantee the efficient use of the sensor nodes and effective access to the collected data. Also, with integration of real time applications in Wireless Senor Networks (WSNs), the use of QoS routing protocols is not only becoming a significant topic, but is also gaining the attention of researchers. In designing an efficient QoS routing protocol, the reliability and guarantee of end-to-end delay are critical events while conserving energy. Thus, considerable research has been focused on designing energy efficient and robust QoS routing protocols. In this paper, we present a state of the art research work based on real-time QoS routing protocols for WMSNs that have already been proposed. This paper categorizes the real-time QoS routing protocols into probabilistic and deterministic protocols. In addition, both categories are classified into soft and hard real time protocols by highlighting the QoS issues including the limitations and features of each protocol. Furthermore, we have compared the performance of mobility-aware query based real-time QoS routing protocols from each category using Network Simulator-2 (NS2). This paper also focuses on the design challenges and future research directions as well as highlights the characteristics of each QoS routing protocol. PMID:26364639

Operational Data Quality Assessment of the Combined PBO, TLALOCNet and COCONet Real-Time GNSS Networks

NASA Astrophysics Data System (ADS)

Hodgkinson, K. M.; Mencin, D.; Fox, O.; Walls, C. P.; Mann, D.; Blume, F.; Berglund, H. T.; Phillips, D.; Meertens, C. M.; Mattioli, G. S.

2015-12-01

The GAGE facility, managed by UNAVCO, currently operates a network of ~460, real-time, high-rate GNSS stations (RT-GNSS). The majority of these RT stations are part of the Earthscope PBO network, which spans the western US Pacific North-American plate boundary. Approximately 50 are distributed throughout the Mexico and Caribbean region funded by the TLALOCNet and COCONet projects. The entire network is processed in real-time at UNAVCO using Precise Point Positioning (PPP). The real-time streams are freely available to all and user demand has grown almost exponentially since 2010. Data usage is multidisciplinary, including tectonic and volcanic deformation studies, meteorological applications, atmospheric science research in addition to use by national, state and commercial entities. 21 RT-GNSS sites in California now include 200-sps accelerometers for the development of Earthquake Early Warning systems. All categories of users of real-time streams have similar requirements, reliable, low-latency, high-rate, and complete data sets. To meet these requirements, UNAVCO tracks the latency and completeness of the incoming raw observations and also is developing tools to monitor the quality of the processed data streams. UNAVCO is currently assessing the precision, accuracy and latency of solutions from various PPP software packages. Also under review are the data formats UNAVCO distributes; for example, the PPP solutions are currently distributed in NMEA format, but other formats such as SEED or GeoJSON may be preferred by different user groups to achieve specific mission objectives. In this presentation we will share our experiences of the challenges involved in the data operations of a continental-scale, multi-project, real-time GNSS network, summarize the network's performance in terms of latency and completeness, and present the comparisons of PPP solutions using different PPP processing techniques.

CISN ShakeAlert: Accounting for site amplification effects and quantifying time and spatial dependence of uncertainty estimates in the Virtual Seismologist earthquake early warning algorithm

NASA Astrophysics Data System (ADS)

Caprio, M.; Cua, G. B.; Wiemer, S.; Fischer, M.; Heaton, T. H.; CISN EEW Team

2011-12-01

The Virtual Seismologist (VS) earthquake early warning (EEW) algorithm is one of 3 EEW approaches being incorporated into the California Integrated Seismic Network (CISN) ShakeAlert system, a prototype EEW system being tested in real-time in California. The VS algorithm, implemented by the Swiss Seismological Service at ETH Zurich, is a Bayesian approach to EEW, wherein the most probable source estimate at any given time is a combination of contributions from a likehihood function that evolves in response to incoming data from the on-going earthquake, and selected prior information, which can include factors such as network topology, the Gutenberg-Richter relationship or previously observed seismicity. The VS codes have been running in real-time at the Southern California Seismic Network (SCSN) since July 2008, and at the Northern California Seismic Network (NCSN) since February 2009. With the aim of improving the convergence of real-time VS magnitude estimates to network magnitudes, we evaluate various empirical and Vs30-based approaches to accounting for site amplification. Empirical station corrections for SCSN stations are derived from M>3.0 events from 2005 through 2009. We evaluate the performance of the various approaches using an independent 2010 dataset. In addition, we analyze real-time VS performance from 2008 to the present to quantify the time and spatial dependence of VS uncertainty estimates. We also summarize real-time VS performance for significant 2011 events in California. Improved magnitude and uncertainty estimates potentially increase the utility of EEW information for end-users, particularly those intending to automate damage-mitigating actions based on real-time information.

Quality of Service for Real-Time Applications Over Next Generation Data Networks

NASA Technical Reports Server (NTRS)

Atiquzzaman, Mohammed; Jain, Raj

2001-01-01

This project, which started on January 1, 2000, was funded by the NASA Glenn Research Center for duration of one year. The deliverables of the project included the following tasks: (1) Study of QoS mapping between the edge and core networks envisioned in the Next Generation networks will provide us with the QoS guarantees that can be obtained from next generation networks; (2) Buffer management techniques to provide strict guarantees to real-time end-to-end applications through preferential treatment to packets belonging to real-time applications. In particular, use of ECN to help reduce the loss on high bandwidth-delay product satellite networks needs to be studied; (3) Effect of Prioritized Packet Discard to increase goodput of the network and reduce the buffering requirements in the ATM switches; (4) Provision of new IP circuit emulation services over Satellite IP backbones using MPLS will be studied; and (5) Determine the architecture and requirements for internetworking ATN and the Next Generation Internet for real-time applications. The project has been completed on time. All the objectives and deliverables of the project have been completed. Research results obtained from this project have been published in a number of papers in journals, conferences, and technical reports, included in this document.

Nonlinearly Activated Neural Network for Solving Time-Varying Complex Sylvester Equation.

PubMed

Li, Shuai; Li, Yangming

2013-10-28

The Sylvester equation is often encountered in mathematics and control theory. For the general time-invariant Sylvester equation problem, which is defined in the domain of complex numbers, the Bartels-Stewart algorithm and its extensions are effective and widely used with an O(n³) time complexity. When applied to solving the time-varying Sylvester equation, the computation burden increases intensively with the decrease of sampling period and cannot satisfy continuous realtime calculation requirements. For the special case of the general Sylvester equation problem defined in the domain of real numbers, gradient-based recurrent neural networks are able to solve the time-varying Sylvester equation in real time, but there always exists an estimation error while a recently proposed recurrent neural network by Zhang et al [this type of neural network is called Zhang neural network (ZNN)] converges to the solution ideally. The advancements in complex-valued neural networks cast light to extend the existing real-valued ZNN for solving the time-varying real-valued Sylvester equation to its counterpart in the domain of complex numbers. In this paper, a complex-valued ZNN for solving the complex-valued Sylvester equation problem is investigated and the global convergence of the neural network is proven with the proposed nonlinear complex-valued activation functions. Moreover, a special type of activation function with a core function, called sign-bi-power function, is proven to enable the ZNN to converge in finite time, which further enhances its advantage in online processing. In this case, the upper bound of the convergence time is also derived analytically. Simulations are performed to evaluate and compare the performance of the neural network with different parameters and activation functions. Both theoretical analysis and numerical simulations validate the effectiveness of the proposed method.

Real-time stress monitoring of highway bridges with a secured wireless sensor network.

DOT National Transportation Integrated Search

2011-12-01

"This collaborative research aims to develop a real-time stress monitoring system for highway bridges with a secured wireless sensor network. The near term goal is to collect wireless sensor data under different traffic patterns from local highway br...

Comparative study of internet cloud and cloudlet over wireless mesh networks for real-time applications

NASA Astrophysics Data System (ADS)

Khan, Kashif A.; Wang, Qi; Luo, Chunbo; Wang, Xinheng; Grecos, Christos

2014-05-01

Mobile cloud computing is receiving world-wide momentum for ubiquitous on-demand cloud services for mobile users provided by Amazon, Google etc. with low capital cost. However, Internet-centric clouds introduce wide area network (WAN) delays that are often intolerable for real-time applications such as video streaming. One promising approach to addressing this challenge is to deploy decentralized mini-cloud facility known as cloudlets to enable localized cloud services. When supported by local wireless connectivity, a wireless cloudlet is expected to offer low cost and high performance cloud services for the users. In this work, we implement a realistic framework that comprises both a popular Internet cloud (Amazon Cloud) and a real-world cloudlet (based on Ubuntu Enterprise Cloud (UEC)) for mobile cloud users in a wireless mesh network. We focus on real-time video streaming over the HTTP standard and implement a typical application. We further perform a comprehensive comparative analysis and empirical evaluation of the application's performance when it is delivered over the Internet cloud and the cloudlet respectively. The study quantifies the influence of the two different cloud networking architectures on supporting real-time video streaming. We also enable movement of the users in the wireless mesh network and investigate the effect of user's mobility on mobile cloud computing over the cloudlet and Amazon cloud respectively. Our experimental results demonstrate the advantages of the cloudlet paradigm over its Internet cloud counterpart in supporting the quality of service of real-time applications.

Near real-time traffic routing

NASA Technical Reports Server (NTRS)

Yang, Chaowei (Inventor); Xie, Jibo (Inventor); Zhou, Bin (Inventor); Cao, Ying (Inventor)

2012-01-01

A near real-time physical transportation network routing system comprising: a traffic simulation computing grid and a dynamic traffic routing service computing grid. The traffic simulator produces traffic network travel time predictions for a physical transportation network using a traffic simulation model and common input data. The physical transportation network is divided into a multiple sections. Each section has a primary zone and a buffer zone. The traffic simulation computing grid includes multiple of traffic simulation computing nodes. The common input data includes static network characteristics, an origin-destination data table, dynamic traffic information data and historical traffic data. The dynamic traffic routing service computing grid includes multiple dynamic traffic routing computing nodes and generates traffic route(s) using the traffic network travel time predictions.

Dynamic quality of service model for improving performance of multimedia real-time transmission in industrial networks.

PubMed

Gopalakrishnan, Ravichandran C; Karunakaran, Manivannan

2014-01-01

Nowadays, quality of service (QoS) is very popular in various research areas like distributed systems, multimedia real-time applications and networking. The requirements of these systems are to satisfy reliability, uptime, security constraints and throughput as well as application specific requirements. The real-time multimedia applications are commonly distributed over the network and meet various time constraints across networks without creating any intervention over control flows. In particular, video compressors make variable bit-rate streams that mismatch the constant-bit-rate channels typically provided by classical real-time protocols, severely reducing the efficiency of network utilization. Thus, it is necessary to enlarge the communication bandwidth to transfer the compressed multimedia streams using Flexible Time Triggered- Enhanced Switched Ethernet (FTT-ESE) protocol. FTT-ESE provides automation to calculate the compression level and change the bandwidth of the stream. This paper focuses on low-latency multimedia transmission over Ethernet with dynamic quality-of-service (QoS) management. This proposed framework deals with a dynamic QoS for multimedia transmission over Ethernet with FTT-ESE protocol. This paper also presents distinct QoS metrics based both on the image quality and network features. Some experiments with recorded and live video streams show the advantages of the proposed framework. To validate the solution we have designed and implemented a simulator based on the Matlab/Simulink, which is a tool to evaluate different network architecture using Simulink blocks.

Real-time data flow and product generating for GNSS

NASA Technical Reports Server (NTRS)

Muellerschoen, Ronald J.; Caissy, Mark

2004-01-01

The last IGS workshop with the theme 'Towards Real-Time' resulted in the design of a prototype for real-time data and sharing within the IGS. A prototype real-time network is being established that will serve as a test bed for real-time activities within the IGS. We review the developments of the prototype and discuss some of the existing methods and related products of real-time GNSS systems. Recommendations are made concerning real-time data distribution and product generation.

A novel spatter detection algorithm based on typical cellular neural network operations for laser beam welding processes

NASA Astrophysics Data System (ADS)

Nicolosi, L.; Abt, F.; Blug, A.; Heider, A.; Tetzlaff, R.; Höfler, H.

2012-01-01

Real-time monitoring of laser beam welding (LBW) has increasingly gained importance in several manufacturing processes ranging from automobile production to precision mechanics. In the latter, a novel algorithm for the real-time detection of spatters was implemented in a camera based on cellular neural networks. The latter can be connected to the optics of commercially available laser machines leading to real-time monitoring of LBW processes at rates up to 15 kHz. Such high monitoring rates allow the integration of other image evaluation tasks such as the detection of the full penetration hole for real-time control of process parameters.

The improved broadband Real-Time Seismic Network in Romania

NASA Astrophysics Data System (ADS)

Neagoe, C.; Ionescu, C.

2009-04-01

Starting with 2002 the National Institute for Earth Physics (NIEP) has developed its real-time digital seismic network. This network consists of 96 seismic stations of which 48 broad band and short period stations and two seismic arrays are transmitted in real-time. The real time seismic stations are equipped with Quanterra Q330 and K2 digitizers, broadband seismometers (STS2, CMG40T, CMG 3ESP, CMG3T) and strong motions sensors Kinemetrics episensors (+/- 2g). SeedLink and AntelopeTM (installed on MARMOT) program packages are used for real-time (RT) data acquisition and exchange. The communication from digital seismic stations to the National Data Center in Bucharest is assured by 5 providers (GPRS, VPN, satellite communication, radio lease line and internet), which will assure the back-up communications lines. The processing centre runs BRTT's AntelopeTM 4.10 data acquisition and processing software on 2 workstations for real-time processing and post processing. The Antelope Real-Time System is also providing automatic event detection, arrival picking, event location and magnitude calculation. It provides graphical display and reporting within near-real-time after a local or regional event occurred. Also at the data center was implemented a system to collect macroseismic information using the internet on which macro seismic intensity maps are generated. In the near future at the data center will be install Seiscomp 3 data acquisition processing software on a workstation. The software will run in parallel with Antelope software as a back-up. The present network will be expanded in the near future. In the first half of 2009 NIEP will install 8 additional broad band stations in Romanian territory, which also will be transmitted to the data center in real time. The Romanian Seismic Network is permanently exchanging real -time waveform data with IRIS, ORFEUS and different European countries through internet. In Romania, magnitude and location of an earthquake are now available within a few minutes after the earthquake occurred. One of the greatest challenges in the near future is to provide shaking intensity maps and other ground motion parameters, within 5 minutes post-event, on the Internet and GIS-based format in order to improve emergency response, public information, preparedness and hazard mitigation

Evaluating the Real-time and Offline Performance of the Virtual Seismologist Earthquake Early Warning Algorithm

NASA Astrophysics Data System (ADS)

Cua, G.; Fischer, M.; Heaton, T.; Wiemer, S.

2009-04-01

The Virtual Seismologist (VS) algorithm is a Bayesian approach to regional, network-based earthquake early warning (EEW). Bayes' theorem as applied in the VS algorithm states that the most probable source estimates at any given time is a combination of contributions from relatively static prior information that does not change over the timescale of earthquake rupture and a likelihood function that evolves with time to take into account incoming pick and amplitude observations from the on-going earthquake. Potentially useful types of prior information include network topology or station health status, regional hazard maps, earthquake forecasts, and the Gutenberg-Richter magnitude-frequency relationship. The VS codes provide magnitude and location estimates once picks are available at 4 stations; these source estimates are subsequently updated each second. The algorithm predicts the geographical distribution of peak ground acceleration and velocity using the estimated magnitude and location and appropriate ground motion prediction equations; the peak ground motion estimates are also updated each second. Implementation of the VS algorithm in California and Switzerland is funded by the Seismic Early Warning for Europe (SAFER) project. The VS method is one of three EEW algorithms whose real-time performance is being evaluated and tested by the California Integrated Seismic Network (CISN) EEW project. A crucial component of operational EEW algorithms is the ability to distinguish between noise and earthquake-related signals in real-time. We discuss various empirical approaches that allow the VS algorithm to operate in the presence of noise. Real-time operation of the VS codes at the Southern California Seismic Network (SCSN) began in July 2008. On average, the VS algorithm provides initial magnitude, location, origin time, and ground motion distribution estimates within 17 seconds of the earthquake origin time. These initial estimate times are dominated by the time for 4 acceptable picks to be available, and thus are heavily influenced by the station density in a given region; these initial estimate times also include the effects of telemetry delay, which ranges between 6 and 15 seconds at the SCSN, and processing time (~1 second). Other relevant performance statistics include: 95% of initial real-time location estimates are within 20 km of the actual epicenter, 97% of initial real-time magnitude estimates are within one magnitude unit of the network magnitude. Extension of real-time VS operations to networks in Northern California is an on-going effort. In Switzerland, the VS codes have been run on offline waveform data from over 125 earthquakes recorded by the Swiss Digital Seismic Network (SDSN) and the Swiss Strong Motion Network (SSMS). We discuss the performance of the VS algorithm on these datasets in terms of magnitude, location, and ground motion estimation.

Development of a Real-Time Intelligent Network Environment.

ERIC Educational Resources Information Center

Gordonov, Anatoliy; Kress, Michael; Klibaner, Roberta

This paper presents a model of an intelligent computer network that provides real-time evaluation of students' performance by incorporating intelligence into the application layer protocol. Specially designed drills allow students to independently solve a number of problems based on current lecture material; students are switched to the most…

A real-time hybrid neuron network for highly parallel cognitive systems.

PubMed

Christiaanse, Gerrit Jan; Zjajo, Amir; Galuzzi, Carlo; van Leuken, Rene

2016-08-01

For comprehensive understanding of how neurons communicate with each other, new tools need to be developed that can accurately mimic the behaviour of such neurons and neuron networks under `real-time' constraints. In this paper, we propose an easily customisable, highly pipelined, neuron network design, which executes optimally scheduled floating-point operations for maximal amount of biophysically plausible neurons per FPGA family type. To reduce the required amount of resources without adverse effect on the calculation latency, a single exponent instance is used for multiple neuron calculation operations. Experimental results indicate that the proposed network design allows the simulation of up to 1188 neurons on Virtex7 (XC7VX550T) device in brain real-time yielding a speed-up of x12.4 compared to the state-of-the art.

Real-time support for high performance aircraft operation

NASA Technical Reports Server (NTRS)

Vidal, Jacques J.

1989-01-01

The feasibility of real-time processing schemes using artificial neural networks (ANNs) is investigated. A rationale for digital neural nets is presented and a general processor architecture for control applications is illustrated. Research results on ANN structures for real-time applications are given. Research results on ANN algorithms for real-time control are also shown.

Evaluation of a Cyber Security System for Hospital Network.

PubMed

Faysel, Mohammad A

2015-01-01

Most of the cyber security systems use simulated data in evaluating their detection capabilities. The proposed cyber security system utilizes real hospital network connections. It uses a probabilistic data mining algorithm to detect anomalous events and takes appropriate response in real-time. On an evaluation using real-world hospital network data consisting of incoming network connections collected for a 24-hour period, the proposed system detected 15 unusual connections which were undetected by a commercial intrusion prevention system for the same network connections. Evaluation of the proposed system shows a potential to secure protected patient health information on a hospital network.

RTX Correction Accuracy and Real-Time Data Processing of the New Integrated SeismoGeodetic System with Real-Time Acceleration and Displacement Measurements for Earthquake Characterization Based on High-Rate Seismic and GPS Data

NASA Astrophysics Data System (ADS)

Zimakov, L. G.; Raczka, J.; Barrientos, S. E.

2016-12-01

We will discuss and show the results obtained from an integrated SeismoGeodetic System, model SG160-09, installed in the Chile (Chilean National Network), Italy (University of Naples Network), and California. The SG160-09 provides the user high rate GNSS and accelerometer data, full epoch-by-epoch measurement integrity and the ability to create combined GNSS and accelerometer high-rate (200Hz) displacement time series in real-time. The SG160-09 combines seismic recording with GNSS geodetic measurement in a single compact, ruggedized case. The system includes a low-power, 220-channel GNSS receiver powered by the latest Trimble-precise Maxwell™6 technology and supports tracking GPS, GLONASS and Galileo signals. The receiver incorporates on-board GNSS point positioning using Real-Time Precise Point Positioning (PPP) technology with satellite clock and orbit corrections delivered over IP networks. The seismic recording includes an ANSS Class A, force balance accelerometer with the latest, low power, 24-bit A/D converter, producing high-resolution seismic data. The SG160-09 processor acquires and packetizes both seismic and geodetic data and transmits it to the central station using an advanced, error-correction protocol providing data integrity between the field and the processing center. The SG160-09 has been installed in three seismic stations in different geographic locations with different Trimble global reference stations coverage The hardware includes the SG160-09 system, external Zephyr Geodetic-2 GNSS antenna, both radio and high-speed Internet communication media. Both acceleration and displacement data was transmitted in real-time to the centralized Data Acquisition Centers for real-time data processing. Command/Control of the field station and real-time GNSS position correction are provided via the Pivot platform. Data from the SG160-09 system was used for seismic event characterization along with data from traditional seismic and geodetic stations installed in the network. Our presentation will focus on the key improvements of the network installation with the SG160-09 system, RTX correction accuracy obtained from Trimble Global RTX tracking network, rapid data transmission, and real-time data processing for strong seismic events and aftershock characterization.

Real-Time Data Filtering and Compression in Wide Area Simulation Networks

DTIC Science & Technology

1992-10-02

Area Simulation Networks Achieving the real-time linkage among multiple , geographically-distant, local area networks that support distributed...November 1989, pp. 52-61. [IEEE85] IEEE/ANSI Standard 8802/3 "Carrier sense multiple access with collision detection (CSMA/CD) access method and...decoding/encoding of multiple bits. The hardware is programmable, easily adaptable and yields a high compression rate. A prototype 2-micron VLSI chip

Real-time optimizations for integrated smart network camera

NASA Astrophysics Data System (ADS)

Desurmont, Xavier; Lienard, Bruno; Meessen, Jerome; Delaigle, Jean-Francois

2005-02-01

We present an integrated real-time smart network camera. This system is composed of an image sensor, an embedded PC based electronic card for image processing and some network capabilities. The application detects events of interest in visual scenes, highlights alarms and computes statistics. The system also produces meta-data information that could be shared between other cameras in a network. We describe the requirements of such a system and then show how the design of the system is optimized to process and compress video in real-time. Indeed, typical video-surveillance algorithms as background differencing, tracking and event detection should be highly optimized and simplified to be used in this hardware. To have a good adequation between hardware and software in this light embedded system, the software management is written on top of the java based middle-ware specification established by the OSGi alliance. We can integrate easily software and hardware in complex environments thanks to the Java Real-Time specification for the virtual machine and some network and service oriented java specifications (like RMI and Jini). Finally, we will report some outcomes and typical case studies of such a camera like counter-flow detection.

QOS-aware error recovery in wireless body sensor networks using adaptive network coding.

PubMed

Razzaque, Mohammad Abdur; Javadi, Saeideh S; Coulibaly, Yahaya; Hira, Muta Tah

2014-12-29

Wireless body sensor networks (WBSNs) for healthcare and medical applications are real-time and life-critical infrastructures, which require a strict guarantee of quality of service (QoS), in terms of latency, error rate and reliability. Considering the criticality of healthcare and medical applications, WBSNs need to fulfill users/applications and the corresponding network's QoS requirements. For instance, for a real-time application to support on-time data delivery, a WBSN needs to guarantee a constrained delay at the network level. A network coding-based error recovery mechanism is an emerging mechanism that can be used in these systems to support QoS at very low energy, memory and hardware cost. However, in dynamic network environments and user requirements, the original non-adaptive version of network coding fails to support some of the network and user QoS requirements. This work explores the QoS requirements of WBSNs in both perspectives of QoS. Based on these requirements, this paper proposes an adaptive network coding-based, QoS-aware error recovery mechanism for WBSNs. It utilizes network-level and user-/application-level information to make it adaptive in both contexts. Thus, it provides improved QoS support adaptively in terms of reliability, energy efficiency and delay. Simulation results show the potential of the proposed mechanism in terms of adaptability, reliability, real-time data delivery and network lifetime compared to its counterparts.

Air-dropped sensor network for real-time high-fidelity volcano monitoring

USGS Publications Warehouse

Song, W.-Z.; Huang, R.; Xu, M.; Ma, A.; Shirazi, B.; LaHusen, R.

2009-01-01

This paper presents the design and deployment experience of an air-dropped wireless sensor network for volcano hazard monitoring. The deployment of five stations into the rugged crater of Mount St. Helens only took one hour with a helicopter. The stations communicate with each other through an amplified 802.15.4 radio and establish a self-forming and self-healing multi-hop wireless network. The distance between stations is up to 2 km. Each sensor station collects and delivers real-time continuous seismic, infrasonic, lightning, GPS raw data to a gateway. The main contribution of this paper is the design and evaluation of a robust sensor network to replace data loggers and provide real-time long-term volcano monitoring. The system supports UTC-time synchronized data acquisition with 1ms accuracy, and is online configurable. It has been tested in the lab environment, the outdoor campus and the volcano crater. Despite the heavy rain, snow, and ice as well as gusts exceeding 120 miles per hour, the sensor network has achieved a remarkable packet delivery ratio above 99% with an overall system uptime of about 93.8% over the 1.5 months evaluation period after deployment. Our initial deployment experiences with the system have alleviated the doubts of domain scientists and prove to them that a low-cost sensor network system can support real-time monitoring in extremely harsh environments. Copyright 2009 ACM.

Real-Time Visualization of Network Behaviors for Situational Awareness

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

Best, Daniel M.; Bohn, Shawn J.; Love, Douglas V.

Plentiful, complex, and dynamic data make understanding the state of an enterprise network difficult. Although visualization can help analysts understand baseline behaviors in network traffic and identify off-normal events, visual analysis systems often do not scale well to operational data volumes (in the hundreds of millions to billions of transactions per day) nor to analysis of emergent trends in real-time data. We present a system that combines multiple, complementary visualization techniques coupled with in-stream analytics, behavioral modeling of network actors, and a high-throughput processing platform called MeDICi. This system provides situational understanding of real-time network activity to help analysts takemore » proactive response steps. We have developed these techniques using requirements gathered from the government users for which the tools are being developed. By linking multiple visualization tools to a streaming analytic pipeline, and designing each tool to support a particular kind of analysis (from high-level awareness to detailed investigation), analysts can understand the behavior of a network across multiple levels of abstraction.« less

Direct modulation of aberrant brain network connectivity through real-time NeuroFeedback

PubMed Central

Kimmich, Sara; Gonzalez-Castillo, Javier; Roopchansingh, Vinai; Popal, Haroon; White, Emily; Gotts, Stephen J; Martin, Alex

2017-01-01

The existence of abnormal connectivity patterns between resting state networks in neuropsychiatric disorders, including Autism Spectrum Disorder (ASD), has been well established. Traditional treatment methods in ASD are limited, and do not address the aberrant network structure. Using real-time fMRI neurofeedback, we directly trained three brain nodes in participants with ASD, in which the aberrant connectivity has been shown to correlate with symptom severity. Desired network connectivity patterns were reinforced in real-time, without participants’ awareness of the training taking place. This training regimen produced large, significant long-term changes in correlations at the network level, and whole brain analysis revealed that the greatest changes were focused on the areas being trained. These changes were not found in the control group. Moreover, changes in ASD resting state connectivity following the training were correlated to changes in behavior, suggesting that neurofeedback can be used to directly alter complex, clinically relevant network connectivity patterns. PMID:28917059

A Comparison Between Publish-and-Subscribe and Client-Server Models in Distributed Control System Networks

NASA Technical Reports Server (NTRS)

Boulanger, Richard P., Jr.; Kwauk, Xian-Min; Stagnaro, Mike; Kliss, Mark (Technical Monitor)

1998-01-01

The BIO-Plex control system requires real-time, flexible, and reliable data delivery. There is no simple "off-the-shelf 'solution. However, several commercial packages will be evaluated using a testbed at ARC for publish- and-subscribe and client-server communication architectures. Point-to-point communication architecture is not suitable for real-time BIO-Plex control system. Client-server architecture provides more flexible data delivery. However, it does not provide direct communication among nodes on the network. Publish-and-subscribe implementation allows direct information exchange among nodes on the net, providing the best time-critical communication. In this work Network Data Delivery Service (NDDS) from Real-Time Innovations, Inc. ARTIE will be used to implement publish-and subscribe architecture. It offers update guarantees and deadlines for real-time data delivery. Bridgestone, a data acquisition and control software package from National Instruments, will be tested for client-server arrangement. A microwave incinerator located at ARC will be instrumented with a fieldbus network of control devices. BridgeVIEW will be used to implement an enterprise server. An enterprise network consisting of several nodes at ARC and a WAN connecting ARC and RISC will then be setup to evaluate proposed control system architectures. Several network configurations will be evaluated for fault tolerance, quality of service, reliability and efficiency. Data acquired from these network evaluation tests will then be used to determine preliminary design criteria for the BIO-Plex distributed control system.

Real-time Adaptive Control Using Neural Generalized Predictive Control

NASA Technical Reports Server (NTRS)

Haley, Pam; Soloway, Don; Gold, Brian

1999-01-01

The objective of this paper is to demonstrate the feasibility of a Nonlinear Generalized Predictive Control algorithm by showing real-time adaptive control on a plant with relatively fast time-constants. Generalized Predictive Control has classically been used in process control where linear control laws were formulated for plants with relatively slow time-constants. The plant of interest for this paper is a magnetic levitation device that is nonlinear and open-loop unstable. In this application, the reference model of the plant is a neural network that has an embedded nominal linear model in the network weights. The control based on the linear model provides initial stability at the beginning of network training. In using a neural network the control laws are nonlinear and online adaptation of the model is possible to capture unmodeled or time-varying dynamics. Newton-Raphson is the minimization algorithm. Newton-Raphson requires the calculation of the Hessian, but even with this computational expense the low iteration rate make this a viable algorithm for real-time control.

Real-Time Support on IEEE 802.11 Wireless Ad-Hoc Networks: Reality vs. Theory

NASA Astrophysics Data System (ADS)

Kang, Mikyung; Kang, Dong-In; Suh, Jinwoo

The usable throughput of an IEEE 802.11 system for an application is much less than the raw bandwidth. Although 802.11b has a theoretical maximum of 11Mbps, more than half of the bandwidth is consumed by overhead leaving at most 5Mbps of usable bandwidth. Considering this characteristic, this paper proposes and analyzes a real-time distributed scheduling scheme based on the existing IEEE 802.11 wireless ad-hoc networks, using USC/ISI's Power Aware Sensing Tracking and Analysis (PASTA) hardware platform. We compared the distributed real-time scheduling scheme with the real-time polling scheme to meet deadline, and compared a measured real bandwidth with a theoretical result. The theoretical and experimental results show that the distributed scheduling scheme can guarantee real-time traffic and enhances the performance up to 74% compared with polling scheme.

Using Antelope and Seiscomp in the framework of the Romanian Seismic Network

NASA Astrophysics Data System (ADS)

Marius Craiu, George; Craiu, Andreea; Marmureanu, Alexandru; Neagoe, Cristian

2014-05-01

The National Institute for Earth Physics (NIEP) operates a real-time seismic network designed to monitor the seismic activity on the Romania territory, dominated by the Vrancea intermediate-depth (60-200 km) earthquakes. The NIEP real-time network currently consists of 102 stations and two seismic arrays equipped with different high quality digitizers (Kinemetrics K2, Quanterra Q330, Quanterra Q330HR, PS6-26, Basalt), broadband and short period seismometers (CMG3ESP, CMG40T, KS2000, KS54000, KS2000, CMG3T, STS2, SH-1, S13, Mark l4c, Ranger, Gs21, Mark 22) and acceleration sensors (Episensor Kinemetrics). The primary goal of the real-time seismic network is to provide earthquake parameters from more broad-band stations with a high dynamic range, for more rapid and accurate computation of the locations and magnitudes of earthquakes. The Seedlink and AntelopeTM program packages are completely automated Antelope seismological system is run at the Data Center in Măgurele. The Antelope data acquisition and processing software is running for real-time processing and post processing. The Antelope real-time system provides automatic event detection, arrival picking, event location, and magnitude calculation. It also provides graphical displays and automatic location within near real time after a local, regional or teleseismic event has occurred SeisComP 3 is another automated system that is run at the NIEP and which provides the following features: data acquisition, data quality control, real-time data exchange and processing, network status monitoring, issuing event alerts, waveform archiving and data distribution, automatic event detection and location, easy access to relevant information about stations, waveforms, and recent earthquakes. The main goal of this paper is to compare both of these data acquisitions systems in order to improve their detection capabilities, location accuracy, magnitude and depth determination and reduce the RMS and other location errors.

Real-time fault diagnosis for propulsion systems

NASA Technical Reports Server (NTRS)

Merrill, Walter C.; Guo, Ten-Huei; Delaat, John C.; Duyar, Ahmet

1991-01-01

Current research toward real time fault diagnosis for propulsion systems at NASA-Lewis is described. The research is being applied to both air breathing and rocket propulsion systems. Topics include fault detection methods including neural networks, system modeling, and real time implementations.

A Network Architecture for Data-Driven Systems

DTIC Science & Technology

1985-07-01

ELABORATION. ..... ..... 26 Real - Time Operating System . ....... ......... 26 Secondary Memory Utilization. ........ ....... 26 Data Flow Graphical...discussions followed by a flight simulator exam~ple. REAL - TIME OPERATING SYSTEM An operating system needs to be designed exclusively for real-time...Assessment. (SDWA) module. The SDWA module is tightly coupled to the real - time operating system . This module must determine the sensitivity to

ADA and multi-microprocessor real-time simulation

NASA Technical Reports Server (NTRS)

Feyock, S.; Collins, W. R.

1983-01-01

The selection of a high-order programming language for a real-time distributed network simulation is described. The additional problem of implementing a language on a possibly changing network is addressed. The recently designed language ADA (trademarked by DoD) was chosen since it provides the best model of the underlying application to be simulated.

Real-time identification of indoor pollutant source positions based on neural network locator of contaminant sources and optimized sensor networks.

PubMed

Vukovic, Vladimir; Tabares-Velasco, Paulo Cesar; Srebric, Jelena

2010-09-01

A growing interest in security and occupant exposure to contaminants revealed a need for fast and reliable identification of contaminant sources during incidental situations. To determine potential contaminant source positions in outdoor environments, current state-of-the-art modeling methods use computational fluid dynamic simulations on parallel processors. In indoor environments, current tools match accidental contaminant distributions with cases from precomputed databases of possible concentration distributions. These methods require intensive computations in pre- and postprocessing. On the other hand, neural networks emerged as a tool for rapid concentration forecasting of outdoor environmental contaminants such as nitrogen oxides or sulfur dioxide. All of these modeling methods depend on the type of sensors used for real-time measurements of contaminant concentrations. A review of the existing sensor technologies revealed that no perfect sensor exists, but intensity of work in this area provides promising results in the near future. The main goal of the presented research study was to extend neural network modeling from the outdoor to the indoor identification of source positions, making this technology applicable to building indoor environments. The developed neural network Locator of Contaminant Sources was also used to optimize number and allocation of contaminant concentration sensors for real-time prediction of indoor contaminant source positions. Such prediction should take place within seconds after receiving real-time contaminant concentration sensor data. For the purpose of neural network training, a multizone program provided distributions of contaminant concentrations for known source positions throughout a test building. Trained networks had an output indicating contaminant source positions based on measured concentrations in different building zones. A validation case based on a real building layout and experimental data demonstrated the ability of this method to identify contaminant source positions. Future research intentions are focused on integration with real sensor networks and model improvements for much more complicated contamination scenarios.

Next generation information communication infrastructure and case studies for future power systems

NASA Astrophysics Data System (ADS)

Qiu, Bin

As power industry enters the new century, powerful driving forces, uncertainties and new functions are compelling electric utilities to make dramatic changes in their information communication infrastructure. Expanding network services such as real time measurement and monitoring are also driving the need for more bandwidth in the communication network. These needs will grow further as new remote real-time protection and control applications become more feasible and pervasive. This dissertation addresses two main issues for the future power system information infrastructure: communication network infrastructure and associated power system applications. Optical networks no doubt will become the predominant data transmission media for next generation power system communication. The rapid development of fiber optic network technology poses new challenges in the areas of topology design, network management and real time applications. Based on advanced fiber optic technologies, an all-fiber network is investigated and proposed. The study will cover the system architecture and data exchange protocol aspects. High bandwidth, robust optical networks could provide great opportunities to the power system for better service and efficient operation. In the dissertation, different applications are investigated. One of the typical applications is the SCADA information accessing system. An Internet-based application for the substation automation system will be presented. VLSI (Very Large Scale Integration) technology is also used for one-line diagrams auto-generation. High transition rate and low latency optical network is especially suitable for power system real time control. In the dissertation, a new local area network based Load Shedding Controller (LSC) for isolated power system will be presented. By using PMU (Phasor Measurement Unit) and fiber optic network, an AGE (Area Generation Error) based accurate wide area load shedding scheme will also be proposed. The objective is to shed the load in the limited area with minimum disturbance.

A Risk Based Approach to Limit the Effects of Covert Channels for Internet Sensor Data Aggregators for Sensor Privacy

NASA Astrophysics Data System (ADS)

Viecco, Camilo H.; Camp, L. Jean

Effective defense against Internet threats requires data on global real time network status. Internet sensor networks provide such real time network data. However, an organization that participates in a sensor network risks providing a covert channel to attackers if that organization’s sensor can be identified. While there is benefit for every party when any individual participates in such sensor deployments, there are perverse incentives against individual participation. As a result, Internet sensor networks currently provide limited data. Ensuring anonymity of individual sensors can decrease the risk of participating in a sensor network without limiting data provision.

A study on efficient detection of network-based IP spoofing DDoS and malware-infected Systems.

PubMed

Seo, Jung Woo; Lee, Sang Jin

2016-01-01

Large-scale network environments require effective detection and response methods against DDoS attacks. Depending on the advancement of IT infrastructure such as the server or network equipment, DDoS attack traffic arising from a few malware-infected systems capable of crippling the organization's internal network has become a significant threat. This study calculates the frequency of network-based packet attributes and analyzes the anomalies of the attributes in order to detect IP-spoofed DDoS attacks. Also, a method is proposed for the effective detection of malware infection systems triggering IP-spoofed DDoS attacks on an edge network. Detection accuracy and performance of the collected real-time traffic on a core network is analyzed thru the use of the proposed algorithm, and a prototype was developed to evaluate the performance of the algorithm. As a result, DDoS attacks on the internal network were detected in real-time and whether or not IP addresses were spoofed was confirmed. Detecting hosts infected by malware in real-time allowed the execution of intrusion responses before stoppage of the internal network caused by large-scale attack traffic.

Real-Time Performance of a Self-Powered Environmental IoT Sensor Network System.

PubMed

Wu, Fan; Rüdiger, Christoph; Yuce, Mehmet Rasit

2017-02-01

Wireless sensor networks (WSNs) play an increasingly important role in monitoring applications in many areas. With the emergence of the Internet-of-Things (IoT), many more lowpower sensors will need to be deployed in various environments to collect and monitor data about environmental factors in real time. Providing power supply to these sensor nodes becomes a critical challenge for realizations of IoT applications as sensor nodes are normally battery-powered and have a limited lifetime. This paper proposes a wireless sensor network that is powered by solar energy harvesting. The sensor network monitors the environmental data with low-power sensor electronics and forms a network using multiple XBee wireless modules. A detailed performance analysis of the network system under solar energy harvesting has been presented. The sensor network system and the proposed energy-harvesting techniques are configured to achieve a continuous energy source for the sensor network. The proposed energy-harvesting system has been successfully designed to enable an energy solution in order to keep sensor nodes active and reliable for a whole day. The paper also outlines some of our experiences in real-time implementation of a sensor network system with energy harvesting.

Real-Time Performance of a Self-Powered Environmental IoT Sensor Network System

PubMed Central

Wu, Fan; Rüdiger, Christoph; Yuce, Mehmet Rasit

2017-01-01

Wireless sensor networks (WSNs) play an increasingly important role in monitoring applications in many areas. With the emergence of the Internet-of-Things (IoT), many more low-power sensors will need to be deployed in various environments to collect and monitor data about environmental factors in real time. Providing power supply to these sensor nodes becomes a critical challenge for realizations of IoT applications as sensor nodes are normally battery-powered and have a limited lifetime. This paper proposes a wireless sensor network that is powered by solar energy harvesting. The sensor network monitors the environmental data with low-power sensor electronics and forms a network using multiple XBee wireless modules. A detailed performance analysis of the network system under solar energy harvesting has been presented. The sensor network system and the proposed energy-harvesting techniques are configured to achieve a continuous energy source for the sensor network. The proposed energy-harvesting system has been successfully designed to enable an energy solution in order to keep sensor nodes active and reliable for a whole day. The paper also outlines some of our experiences in real-time implementation of a sensor network system with energy harvesting. PMID:28157148

Robust Analysis of Network-Based Real-Time Kinematic for GNSS-Derived Heights.

PubMed

Bae, Tae-Suk; Grejner-Brzezinska, Dorota; Mader, Gerald; Dennis, Michael

2015-10-26

New guidelines and procedures for real-time (RT) network-based solutions are required in order to support Global Navigation Satellite System (GNSS) derived heights. Two kinds of experiments were carried out to analyze the performance of the network-based real-time kinematic (RTK) solutions. New test marks were installed in different surrounding environments, and the existing GPS benchmarks were used for analyzing the effect of different factors, such as baseline lengths, antenna types, on the final accuracy and reliability of the height estimation. The RT solutions are categorized into three groups: single-base RTK, multiple-epoch network RTK (mRTN), and single-epoch network RTK (sRTN). The RTK solution can be biased up to 9 mm depending on the surrounding environment, but there was no notable bias for a longer reference base station (about 30 km) In addition, the occupation time for the network RTK was investigated in various cases. There is no explicit bias in the solution for different durations, but smoother results were obtained for longer durations. Further investigation is needed into the effect of changing the occupation time between solutions and into the possibility of using single-epoch solutions in precise determination of heights by GNSS.

[Research on Zhejiang blood information network and management system].

PubMed

Yan, Li-Xing; Xu, Yan; Meng, Zhong-Hua; Kong, Chang-Hong; Wang, Jian-Min; Jin, Zhen-Liang; Wu, Shi-Ding; Chen, Chang-Shui; Luo, Ling-Fei

2007-02-01

This research was aimed to develop the first level blood information centralized database and real time communication network at a province area in China. Multiple technology like local area network database separate operation, real time data concentration and distribution mechanism, allopatric backup, and optical fiber virtual private network (VPN) were used. As a result, the blood information centralized database and management system were successfully constructed, which covers all the Zhejiang province, and the real time exchange of blood data was realised. In conclusion, its implementation promote volunteer blood donation and ensure the blood safety in Zhejiang, especially strengthen the quick response to public health emergency. This project lays the first stone of centralized test and allotment among blood banks in Zhejiang, and can serve as a reference of contemporary blood bank information systems in China.

Building a Smartphone Seismic Network

NASA Astrophysics Data System (ADS)

Kong, Q.; Allen, R. M.

2013-12-01

We are exploring to build a new type of seismic network by using the smartphones. The accelerometers in smartphones can be used to record earthquakes, the GPS unit can give an accurate location, and the built-in communication unit makes the communication easier for this network. In the future, these smartphones may work as a supplement network to the current traditional network for scientific research and real-time applications. In order to build this network, we developed an application for android phones and server to record the acceleration in real time. These records can be sent back to a server in real time, and analyzed at the server. We evaluated the performance of the smartphone as a seismic recording instrument by comparing them with high quality accelerometer while located on controlled shake tables for a variety of tests, and also the noise floor test. Based on the daily human activity data recorded by the volunteers and the shake table tests data, we also developed algorithm for the smartphones to detect earthquakes from daily human activities. These all form the basis of setting up a new prototype smartphone seismic network in the near future.

Applications of Temporal Graph Metrics to Real-World Networks

NASA Astrophysics Data System (ADS)

Tang, John; Leontiadis, Ilias; Scellato, Salvatore; Nicosia, Vincenzo; Mascolo, Cecilia; Musolesi, Mirco; Latora, Vito

Real world networks exhibit rich temporal information: friends are added and removed over time in online social networks; the seasons dictate the predator-prey relationship in food webs; and the propagation of a virus depends on the network of human contacts throughout the day. Recent studies have demonstrated that static network analysis is perhaps unsuitable in the study of real world network since static paths ignore time order, which, in turn, results in static shortest paths overestimating available links and underestimating their true corresponding lengths. Temporal extensions to centrality and efficiency metrics based on temporal shortest paths have also been proposed. Firstly, we analyse the roles of key individuals of a corporate network ranked according to temporal centrality within the context of a bankruptcy scandal; secondly, we present how such temporal metrics can be used to study the robustness of temporal networks in presence of random errors and intelligent attacks; thirdly, we study containment schemes for mobile phone malware which can spread via short range radio, similar to biological viruses; finally, we study how the temporal network structure of human interactions can be exploited to effectively immunise human populations. Through these applications we demonstrate that temporal metrics provide a more accurate and effective analysis of real-world networks compared to their static counterparts.

UNAVCO Real-Time GNSS Positioning: High-Precision Static and Kinematic Testing of the Next Generation GNSS network.

NASA Astrophysics Data System (ADS)

Berglund, H. T.; Hodgkinson, K. M.; Blume, F.; Mencin, D.; Phillips, D. A.; Meertens, C. M.; Mattioli, G. S.

2014-12-01

The GAGE Facility, managed by UNAVCO, operates a real-time GNSS (RT-GNSS) network of ~450 stations. The majority of the streaming stations are part of the EarthScope Plate Boundary Observatory (PBO). Following community input from a real-time GNSS data products and formats meeting hosted by UNAVCO in Spring of 2011, UNAVCO now provides real-time PPP positions, and network solutions where practical, for all available stations using Trimble's PIVOT RTX server software and TrackRT. The UNAVCO real-time system has the potential to enhance our understanding of earthquakes, seismic wave propagation, volcanic eruptions, magmatic intrusions, movement of ice, landslides, and the dynamics of the atmosphere. Beyond the ever increasing applications in science and engineering, RT-GNSS has the potential to provide early warning of hazards to emergency managers, utilities, other infrastructure managers, first responders and others. Upgrades to the network include eight Trimble NetR9 GNSS receivers with GLONASS and receiver-based RTX capabilities and sixteen new co-located MEMS based accelerometers. These new capabilities will allow integration of GNSS and strong motion data to produce broad-spectrum waveforms improving Earthquake Early Warning systems. Controlled outdoor kinematic and static experiments provide a useful method for evaluating and comparing real-time systems. UNAVCO has developed a portable low-cost antenna actuator to characterize the kinematic performance of receiver- and server-based real-time positioning algorithms and identify system limitations. We have performed tests using controlled 1-d antenna motions and will present comparisons between these and other post-processed kinematic algorithms including GIPSY-OASIS and TRACK. In addition to kinematic testing, long-term static testing of Trimble's RTX service is ongoing at UNAVCO and will be used to characterize the stability of the position time-series produced by RTX. In addition, with the goal of characterizing stability and improving software and higher level products based on real-time and high frequency GNSS time series, we present an overview of the UNAVCO RT-GPS system, a comparison of the UNAVCO generated real-time, static and community data products, and an overview of available common data sets.

Data Centric Sensor Stream Reduction for Real-Time Applications in Wireless Sensor Networks

PubMed Central

Aquino, Andre Luiz Lins; Nakamura, Eduardo Freire

2009-01-01

This work presents a data-centric strategy to meet deadlines in soft real-time applications in wireless sensor networks. This strategy considers three main aspects: (i) The design of real-time application to obtain the minimum deadlines; (ii) An analytic model to estimate the ideal sample size used by data-reduction algorithms; and (iii) Two data-centric stream-based sampling algorithms to perform data reduction whenever necessary. Simulation results show that our data-centric strategies meet deadlines without loosing data representativeness. PMID:22303145

[Research and implementation of a real-time monitoring system for running status of medical monitors based on the internet of things].

PubMed

Li, Yiming; Qian, Mingli; Li, Long; Li, Bin

2014-07-01

This paper proposed a real-time monitoring system for running status of medical monitors based on the internet of things. In the aspect of hardware, a solution of ZigBee networks plus 470 MHz networks is proposed. In the aspect of software, graphical display of monitoring interface and real-time equipment failure alarm is implemented. The system has the function of remote equipment failure detection and wireless localization, which provides a practical and effective method for medical equipment management.

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.

Smart-Grid Backbone Network Real-Time Delay Reduction via Integer Programming.

PubMed

Pagadrai, Sasikanth; Yilmaz, Muhittin; Valluri, Pratyush

2016-08-01

This research investigates an optimal delay-based virtual topology design using integer linear programming (ILP), which is applied to the current backbone networks such as smart-grid real-time communication systems. A network traffic matrix is applied and the corresponding virtual topology problem is solved using the ILP formulations that include a network delay-dependent objective function and lightpath routing, wavelength assignment, wavelength continuity, flow routing, and traffic loss constraints. The proposed optimization approach provides an efficient deterministic integration of intelligent sensing and decision making, and network learning features for superior smart grid operations by adaptively responding the time-varying network traffic data as well as operational constraints to maintain optimal virtual topologies. A representative optical backbone network has been utilized to demonstrate the proposed optimization framework whose simulation results indicate that superior smart-grid network performance can be achieved using commercial networks and integer programming.

Classification of complex networks based on similarity of topological network features

NASA Astrophysics Data System (ADS)

Attar, Niousha; Aliakbary, Sadegh

2017-09-01

Over the past few decades, networks have been widely used to model real-world phenomena. Real-world networks exhibit nontrivial topological characteristics and therefore, many network models are proposed in the literature for generating graphs that are similar to real networks. Network models reproduce nontrivial properties such as long-tail degree distributions or high clustering coefficients. In this context, we encounter the problem of selecting the network model that best fits a given real-world network. The need for a model selection method reveals the network classification problem, in which a target-network is classified into one of the candidate network models. In this paper, we propose a novel network classification method which is independent of the network size and employs an alignment-free metric of network comparison. The proposed method is based on supervised machine learning algorithms and utilizes the topological similarities of networks for the classification task. The experiments show that the proposed method outperforms state-of-the-art methods with respect to classification accuracy, time efficiency, and robustness to noise.

QoS for Real Time Applications over Next Generation Data Networks

NASA Technical Reports Server (NTRS)

Ivancic, William; Atiquzzaman, Mohammed; Bai, Haowei; Su, Hongjun; Chitri, Jyotsna; Ahamed, Faruque

2001-01-01

Viewgraphs on Qualtity of Service (QOS) for real time applications over next generation data networks are presented. The progress to date include: Task 1: QoS in Integrated Services over DiffServ networks (UD); Task 2: Interconnecting ATN with the next generation Internet (UD); Task 3: QoS in DiffServ over ATM (UD); Task 4: Improving Explicit Congestion Notification with the Mark-Front Strategy (OSU); Task 5: Multiplexing VBR over VBR (OSU); and Task 6: Achieving QoS for TCP traffic in Satellite Networks with Differentiated Services (OSU).

Research on moving target defense based on SDN

NASA Astrophysics Data System (ADS)

Chen, Mingyong; Wu, Weimin

2017-08-01

An address mutation strategy was proposed. This strategy provided an unpredictable change in address, replacing the real address of the packet forwarding process and path mutation, thus hiding the real address of the host and path. a mobile object defense technology based on Spatio-temporal Mutation on this basis is proposed, Using the software Defined Network centralized control architecture advantage combines sFlow traffic monitoring technology and Moving Target Defense. A mutated time period which can be changed in real time according to the network traffic is changed, and the destination address is changed while the controller abruptly changes the address while the data packet is transferred between the switches to construct a moving target, confusing the host within the network, thereby protecting the host and network.

Impact of different cloud deployments on real-time video applications for mobile video cloud users

NASA Astrophysics Data System (ADS)

Khan, Kashif A.; Wang, Qi; Luo, Chunbo; Wang, Xinheng; Grecos, Christos

2015-02-01

The latest trend to access mobile cloud services through wireless network connectivity has amplified globally among both entrepreneurs and home end users. Although existing public cloud service vendors such as Google, Microsoft Azure etc. are providing on-demand cloud services with affordable cost for mobile users, there are still a number of challenges to achieve high-quality mobile cloud based video applications, especially due to the bandwidth-constrained and errorprone mobile network connectivity, which is the communication bottleneck for end-to-end video delivery. In addition, existing accessible clouds networking architectures are different in term of their implementation, services, resources, storage, pricing, support and so on, and these differences have varied impact on the performance of cloud-based real-time video applications. Nevertheless, these challenges and impacts have not been thoroughly investigated in the literature. In our previous work, we have implemented a mobile cloud network model that integrates localized and decentralized cloudlets (mini-clouds) and wireless mesh networks. In this paper, we deploy a real-time framework consisting of various existing Internet cloud networking architectures (Google Cloud, Microsoft Azure and Eucalyptus Cloud) and a cloudlet based on Ubuntu Enterprise Cloud over wireless mesh networking technology for mobile cloud end users. It is noted that the increasing trend to access real-time video streaming over HTTP/HTTPS is gaining popularity among both research and industrial communities to leverage the existing web services and HTTP infrastructure in the Internet. To study the performance under different deployments using different public and private cloud service providers, we employ real-time video streaming over the HTTP/HTTPS standard, and conduct experimental evaluation and in-depth comparative analysis of the impact of different deployments on the quality of service for mobile video cloud users. Empirical results are presented and discussed to quantify and explain the different impacts resulted from various cloud deployments, video application and wireless/mobile network setting, and user mobility. Additionally, this paper analyses the advantages, disadvantages, limitations and optimization techniques in various cloud networking deployments, in particular the cloudlet approach compared with the Internet cloud approach, with recommendations of optimized deployments highlighted. Finally, federated clouds and inter-cloud collaboration challenges and opportunities are discussed in the context of supporting real-time video applications for mobile users.

Real-time Data Access to First Responders: A VORB application

NASA Astrophysics Data System (ADS)

Lu, S.; Kim, J. B.; Bryant, P.; Foley, S.; Vernon, F.; Rajasekar, A.; Meier, S.

2006-12-01

Getting information to first responders is not an easy task. The sensors that provide the information are diverse in formats and come from many disciplines. They are also distributed by location, transmit data at different frequencies and are managed and owned by autonomous administrative entities. Pulling such types of data in real-time, needs a very robust sensor network with reliable data transport and buffering capabilities. Moreover, the system should be extensible and scalable in numbers and sensor types. ROADNet is a real- time sensor network project at UCSD gathering diverse environmental data in real-time or near-real-time. VORB (Virtual Object Ring Buffer) is the middleware used in ROADNet offering simple, uniform and scalable real-time data management for discovering (through metadata), accessing and archiving real-time data and data streams. Recent development in VORB, a web API, has offered quick and simple real-time data integration with web applications. In this poster, we discuss one application developed as part of ROADNet. SMER (Santa Margarita Ecological Reserve) is located in interior Southern California, a region prone to catastrophic wildfires each summer and fall. To provide data during emergencies, we have applied the VORB framework to develop a web-based application for providing access to diverse sensor data including weather data, heat sensor information, and images from cameras. Wildfire fighters have access to real-time data about weather and heat conditions in the area and view pictures taken from cameras at multiple points in the Reserve to pinpoint problem areas. Moreover, they can browse archived images and sensor data from earlier times to provide a comparison framework. To show scalability of the system, we have expanded the sensor network under consideration through other areas in Southern California including sensors accessible by Los Angeles County Fire Department (LACOFD) and those available through the High Performance Wireless Research and Education Network (HPWREN). The poster will discuss the system architecture and components, the types of sensor being used and usage scenarios. The system is currently operational through the SMER web-site.

Using real time traveler demand data to optimize commuter rail feeder systems.

DOT National Transportation Integrated Search

2012-08-01

"This report focuses on real time optimization of the Commuter Rail Circulator Route Network Design Problem (CRCNDP). The route configuration of the circulator system where to stop and the route among the stops is determined on a real-time ba...

Network Design and Performance of the System Integration Test, Linked Simulators Phase.

DTIC Science & Technology

1998-01-01

community has primarily used UNIX systems. UNIX is not a real - time operating system and thus very accurate time stamping, i.e., millisecond accuracy, is... time operating system works against us. The clock time on the UNIX workstations drifts from the UTC standard over time and this drift varies from...loggers at each site use the Network Time Protocol to synchronize to the master clock on a workstation in the TCAC. Again, the fact that UNIX is not a real

Neural network evaluation of tokamak current profiles for real time control

NASA Astrophysics Data System (ADS)

Wróblewski, Dariusz

1997-02-01

Active feedback control of the current profile, requiring real-time determination of the current profile parameters, is envisioned for tokamaks operating in enhanced confinement regimes. The distribution of toroidal current in a tokamak is now routinely evaluated based on external (magnetic probes, flux loops) and internal (motional Stark effect) measurements of the poloidal magnetic field. However, the analysis involves reconstruction of magnetohydrodynamic equilibrium and is too intensive computationally to be performed in real time. In the present study, a neural network is used to provide a mapping from the magnetic measurements (internal and external) to selected parameters of the safety factor profile. The single-pass, feedforward calculation of output of a trained neural network is very fast, making this approach particularly suitable for real-time applications. The network was trained on a large set of simulated equilibrium data for the DIII-D tokamak. The database encompasses a large variety of current profiles including the hollow current profiles important for reversed central shear operation. The parameters of safety factor profile (a quantity related to the current profile through the magnetic field tilt angle) estimated by the neural network include central safety factor, q0, minimum value of q, qmin, and the location of qmin. Very good performance of the trained neural network both for simulated test data and for experimental datais demonstrated.

Neural network evaluation of tokamak current profiles for real time control (abstract)

NASA Astrophysics Data System (ADS)

Wróblewski, Dariusz

1997-01-01

Active feedback control of the current profile, requiring real-time determination of the current profile parameters, is envisioned for tokamaks operating in enhanced confinement regimes. The distribution of toroidal current in a tokamak is now routinely evaluated based on external (magnetic probes, flux loops) and internal (motional Stark effect) measurements of the poloidal magnetic field. However, the analysis involves reconstruction of magnetohydrodynamic equilibrium and is too intensive computationally to be performed in real time. In the present study, a neural network is used to provide a mapping from the magnetic measurements (internal and external) to selected parameters of the safety factor profile. The single-pass, feedforward calculation of output of a trained neural network is very fast, making this approach particularly suitable for real-time applications. The network was trained on a large set of simulated equilibrium data for the DIII-D tokamak. The database encompasses a large variety of current profiles including the hollow current profiles important for reversed central shear operation. The parameters of safety factor profile (a quantity related to the current profile through the magnetic field tilt angle) estimated by the neural network include central safety factor, q0, minimum value of q, qmin, and the location of qmin. Very good performance of the trained neural network both for simulated test data and for experimental data is demonstrated.

Enhancing the Quality of Service for Real Time Traffic over Optical Burst Switching (OBS) Networks with Ensuring the Fairness for Other Traffics.

PubMed

Al-Shargabi, Mohammed A; Shaikh, Asadullah; Ismail, Abdulsamad S

2016-01-01

Optical burst switching (OBS) networks have been attracting much consideration as a promising approach to build the next generation optical Internet. A solution for enhancing the Quality of Service (QoS) for high priority real time traffic over OBS with the fairness among the traffic types is absent in current OBS' QoS schemes. In this paper we present a novel Real Time Quality of Service with Fairness Ratio (RT-QoSFR) scheme that can adapt the burst assembly parameters according to the traffic QoS needs in order to enhance the real time traffic QoS requirements and to ensure the fairness for other traffic. The results show that RT-QoSFR scheme is able to fulfill the real time traffic requirements (end to end delay, and loss rate) ensuring the fairness for other traffics under various conditions such as the type of real time traffic and traffic load. RT-QoSFR can guarantee that the delay of the real time traffic packets does not exceed the maximum packets transfer delay value. Furthermore, it can reduce the real time traffic packets loss, at the same time guarantee the fairness for non real time traffic packets by determining the ratio of real time traffic inside the burst to be 50-60%, 30-40%, and 10-20% for high, normal, and low traffic loads respectively.

Development and application of network virtual instrument for emission spectrum of pulsed high-voltage direct current discharge

NASA Astrophysics Data System (ADS)

Gong, X.; Wu, Q.

2017-12-01

Network virtual instrument (VI) is a new development direction in current automated test. Based on LabVIEW, the software and hardware system of VI used for emission spectrum of pulsed high-voltage direct current (DC) discharge is developed and applied to investigate pulsed high-voltage DC discharge of nitrogen. By doing so, various functions are realized including real time collection of emission spectrum of nitrogen, monitoring operation state of instruments and real time analysis and processing of data. By using shared variables and DataSocket technology in LabVIEW, the network VI system based on field VI is established. The system can acquire the emission spectrum of nitrogen in the test site, monitor operation states of field instruments, realize real time face-to-face interchange of two sites, and analyze data in the far-end from the network terminal. By employing the network VI system, the staff in the two sites acquired the same emission spectrum of nitrogen and conducted the real time communication. By comparing with the previous results, it can be seen that the experimental data obtained by using the system are highly precise. This implies that the system shows reliable network stability and safety and satisfies the requirements for studying the emission spectrum of pulsed high-voltage discharge in high-precision fields or network terminals. The proposed architecture system is described and the target group gets the useful enlightenment in many fields including engineering remote users, specifically in control- and automation-related tasks.

Modified neural networks for rapid recovery of tokamak plasma parameters for real time control

NASA Astrophysics Data System (ADS)

Sengupta, A.; Ranjan, P.

2002-07-01

Two modified neural network techniques are used for the identification of the equilibrium plasma parameters of the Superconducting Steady State Tokamak I from external magnetic measurements. This is expected to ultimately assist in a real time plasma control. As different from the conventional network structure where a single network with the optimum number of processing elements calculates the outputs, a multinetwork system connected in parallel does the calculations here in one of the methods. This network is called the double neural network. The accuracy of the recovered parameters is clearly more than the conventional network. The other type of neural network used here is based on the statistical function parametrization combined with a neural network. The principal component transformation removes linear dependences from the measurements and a dimensional reduction process reduces the dimensionality of the input space. This reduced and transformed input set, rather than the entire set, is fed into the neural network input. This is known as the principal component transformation-based neural network. The accuracy of the recovered parameters in the latter type of modified network is found to be a further improvement over the accuracy of the double neural network. This result differs from that obtained in an earlier work where the double neural network showed better performance. The conventional network and the function parametrization methods have also been used for comparison. The conventional network has been used for an optimization of the set of magnetic diagnostics. The effective set of sensors, as assessed by this network, are compared with the principal component based network. Fault tolerance of the neural networks has been tested. The double neural network showed the maximum resistance to faults in the diagnostics, while the principal component based network performed poorly. Finally the processing times of the methods have been compared. The double network and the principal component network involve the minimum computation time, although the conventional network also performs well enough to be used in real time.

Impact of ultrasound video transfer on the practice of ultrasound

NASA Astrophysics Data System (ADS)

Duerinckx, Andre J.; Hayrapetian, Alek S.; Grant, Edward G.; Valentino, Daniel J.; Rahbar, Darius; Kiszonas, Mike; Franco, Ricky; Melany, Michelle; Narin, Sherelle L.; Ragavendra, Nagesh

1996-05-01

Sonography can be highly dependent on real-time imaging and as such is highly physician intensive. Such situations arise mostly during complicated ultrasound radiology studies or echocardiology examinations. Under those circumstances it would be of benefit to transmit real-time images beyond the immediate area of the ultrasound laboratory when a physician is not on location. We undertook this study to determine if both static and dynamic image transfer to remote locations might be accomplished using an ultrafast ATM network and PACS. Image management of the local image files was performed by a commercial PACS from AGFA corporation. The local network was Ethernet based, and the global network was based on Asynchronous Transfer Mode (ATM, rates up to 100 Mbits/sec). Real-time image transfer involved two teaching hospitals, one of which had 2 separate ultrasound facilities. Radiologists consulted with technologists via telephone while the examinations were being performed. The applications of ATM network providing real time video for ultrasound imaging in a clinical environment and its potential impact on health delivery and clinical teaching. This technology increased technologist and physician productivity due to the elimination of commute time for physicians and waiting time for technologists and patients. Physician confidence in diagnosis increased compared to reviewing static images alone. This system provided instant access for radiologists to real-time scans from remote sites. Image quality and frame rate were equivalent to the original. The system increased productivity by allowing physicians to monitor studies at multiple sites simultaneously.

A New Generation of Real-Time Systems in the JET Tokamak

NASA Astrophysics Data System (ADS)

Alves, Diogo; Neto, Andre C.; Valcarcel, Daniel F.; Felton, Robert; Lopez, Juan M.; Barbalace, Antonio; Boncagni, Luca; Card, Peter; De Tommasi, Gianmaria; Goodyear, Alex; Jachmich, Stefan; Lomas, Peter J.; Maviglia, Francesco; McCullen, Paul; Murari, Andrea; Rainford, Mark; Reux, Cedric; Rimini, Fernanda; Sartori, Filippo; Stephen, Adam V.; Vega, Jesus; Vitelli, Riccardo; Zabeo, Luca; Zastrow, Klaus-Dieter

2014-04-01

Recently, a new recipe for developing and deploying real-time systems has become increasingly adopted in the JET tokamak. Powered by the advent of x86 multi-core technology and the reliability of JET's well established Real-Time Data Network (RTDN) to handle all real-time I/O, an official Linux vanilla kernel has been demonstrated to be able to provide real-time performance to user-space applications that are required to meet stringent timing constraints. In particular, a careful rearrangement of the Interrupt ReQuests' (IRQs) affinities together with the kernel's CPU isolation mechanism allows one to obtain either soft or hard real-time behavior depending on the synchronization mechanism adopted. Finally, the Multithreaded Application Real-Time executor (MARTe) framework is used for building applications particularly optimised for exploring multi-core architectures. In the past year, four new systems based on this philosophy have been installed and are now part of JET's routine operation. The focus of the present work is on the configuration aspects that enable these new systems' real-time capability. Details are given about the common real-time configuration of these systems, followed by a brief description of each system together with results regarding their real-time performance. A cycle time jitter analysis of a user-space MARTe based application synchronizing over a network is also presented. The goal is to compare its deterministic performance while running on a vanilla and on a Messaging Real time Grid (MRG) Linux kernel.

An FPGA Platform for Real-Time Simulation of Spiking Neuronal Networks

PubMed Central

Pani, Danilo; Meloni, Paolo; Tuveri, Giuseppe; Palumbo, Francesca; Massobrio, Paolo; Raffo, Luigi

2017-01-01

In the last years, the idea to dynamically interface biological neurons with artificial ones has become more and more urgent. The reason is essentially due to the design of innovative neuroprostheses where biological cell assemblies of the brain can be substituted by artificial ones. For closed-loop experiments with biological neuronal networks interfaced with in silico modeled networks, several technological challenges need to be faced, from the low-level interfacing between the living tissue and the computational model to the implementation of the latter in a suitable form for real-time processing. Field programmable gate arrays (FPGAs) can improve flexibility when simple neuronal models are required, obtaining good accuracy, real-time performance, and the possibility to create a hybrid system without any custom hardware, just programming the hardware to achieve the required functionality. In this paper, this possibility is explored presenting a modular and efficient FPGA design of an in silico spiking neural network exploiting the Izhikevich model. The proposed system, prototypically implemented on a Xilinx Virtex 6 device, is able to simulate a fully connected network counting up to 1,440 neurons, in real-time, at a sampling rate of 10 kHz, which is reasonable for small to medium scale extra-cellular closed-loop experiments. PMID:28293163

Real-Time Alpine Measurement System Using Wireless Sensor Networks

PubMed Central

2017-01-01

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra’s wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km2 network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape. PMID:29120376

Real-Time Alpine Measurement System Using Wireless Sensor Networks.

PubMed

Malek, Sami A; Avanzi, Francesco; Brun-Laguna, Keoma; Maurer, Tessa; Oroza, Carlos A; Hartsough, Peter C; Watteyne, Thomas; Glaser, Steven D

2017-11-09

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra's wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km 2 network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape.

Random walks on activity-driven networks with attractiveness

NASA Astrophysics Data System (ADS)

Alessandretti, Laura; Sun, Kaiyuan; Baronchelli, Andrea; Perra, Nicola

2017-05-01

Virtually all real-world networks are dynamical entities. In social networks, the propensity of nodes to engage in social interactions (activity) and their chances to be selected by active nodes (attractiveness) are heterogeneously distributed. Here, we present a time-varying network model where each node and the dynamical formation of ties are characterized by these two features. We study how these properties affect random-walk processes unfolding on the network when the time scales describing the process and the network evolution are comparable. We derive analytical solutions for the stationary state and the mean first-passage time of the process, and we study cases informed by empirical observations of social networks. Our work shows that previously disregarded properties of real social systems, such as heterogeneous distributions of activity and attractiveness as well as the correlations between them, substantially affect the dynamical process unfolding on the network.

The Study on the Communication Network of Wide Area Measurement System in Electricity Grid

NASA Astrophysics Data System (ADS)

Xiaorong, Cheng; Ying, Wang; Yangdan, Ni

Wide area measurement system(WAMS) is a fundamental part of security defense in Smart Grid, and the communication system of WAMS is an important part of Electric power communication network. For a large regional network is concerned, the real-time data which is transferred in the communication network of WAMS will affect the safe operation of the power grid directly. Therefore, WAMS raised higher requirements for real-time, reliability and security to its communication network. In this paper, the architecture of WASM communication network was studied according to the seven layers model of the open systems interconnection(OSI), and the network architecture was researched from all levels. We explored the media of WAMS communication network, the network communication protocol and network technology. Finally, the delay of the network were analyzed.

Georgia's Surface-Water Resources and Streamflow Monitoring Network, 2006

USGS Publications Warehouse

Nobles, Patricia L.; ,

2006-01-01

The U.S. Geological Survey (USGS) network of 223 real-time monitoring stations, the 'Georgia HydroWatch,' provides real-time water-stage data, with streamflow computed at 198 locations, and rainfall recorded at 187 stations. These sites continuously record data on 15-minute intervals and transmit the data via satellite to be incorporated into the USGS National Water Information System database. These data are automatically posted to the USGS Web site for public dissemination (http://waterdata.usgs.gov/ga/nwis/nwis). The real-time capability of this network provides information to help emergency-management officials protect human life and property during floods, and mitigate the effects of prolonged drought. The map at right shows the USGS streamflow monitoring network for Georgia and major watersheds. Streamflow is monitored at 198 sites statewide, more than 80 percent of which include precipitation gages. Various Federal, State, and local agencies fund these streamflow monitoring stations.

Developing a robust wireless sensor network structure for environmental sensing

NASA Astrophysics Data System (ADS)

Zhang, Z.; Oroza, C.; Glaser, S. D.; Bales, R. C.; Conklin, M. H.

2013-12-01

The American River Hydrologic Observatory is being strategically deployed as a real-time ground-based measurement network that delivers accurate and timely information on snow conditions and other hydrologic attributes with a previously unheard of granularity of time and space. The basin-scale network involves 18 sub-networks set out at physiographically representative locations spanning the seasonally snow-covered half of the 5000 km2 American river basin. Each sub-network, covering about a 1-km2 area, consists of 10 wirelessly networked sensing nodes that continuously measure and telemeter temperature, and snow depth; plus selected locations are equipped with sensors for relative humidity, solar radiation, and soil moisture at several depths. The sensor locations were chosen to maximize the variance sampled for snow depth within the basin. Network design and deployment involves an iterative but efficient process. After sensor-station locations are determined, a robust network of interlinking sensor stations and signal repeaters must be constructed to route sensor data to a central base station with a two-way communicable data uplink. Data can then be uploaded from site to remote servers in real time through satellite and cell modems. Signal repeaters are placed for robustness of a self-healing network with redundant signal paths to the base station. Manual, trial-and-error heuristic approaches for node placement are inefficient and labor intensive. In that approach field personnel must restructure the network in real time and wait for new network statistics to be calculated at the base station before finalizing a placement, acting without knowledge of the global topography or overall network structure. We show how digital elevation plus high-definition aerial photographs to give foliage coverage can optimize planning of signal repeater placements and guarantee a robust network structure prior to the physical deployment. We can also 'stress test' the final network by simulating the failure of an individual node and investigating the effect and the self-healing ability of the stressed network. The resulting sensor network can survive temporary service interruption from a small subset of signal repeaters and sensor stations. The robustness and the resilient of the network performance ensure the integrity of the dataset and the real-time transmissibility during harsh conditions.

Trade-offs between driving nodes and time-to-control in complex networks

PubMed Central

Pequito, Sérgio; Preciado, Victor M.; Barabási, Albert-László; Pappas, George J.

2017-01-01

Recent advances in control theory provide us with efficient tools to determine the minimum number of driving (or driven) nodes to steer a complex network towards a desired state. Furthermore, we often need to do it within a given time window, so it is of practical importance to understand the trade-offs between the minimum number of driving/driven nodes and the minimum time required to reach a desired state. Therefore, we introduce the notion of actuation spectrum to capture such trade-offs, which we used to find that in many complex networks only a small fraction of driving (or driven) nodes is required to steer the network to a desired state within a relatively small time window. Furthermore, our empirical studies reveal that, even though synthetic network models are designed to present structural properties similar to those observed in real networks, their actuation spectra can be dramatically different. Thus, it supports the need to develop new synthetic network models able to replicate controllability properties of real-world networks. PMID:28054597

Trade-offs between driving nodes and time-to-control in complex networks

NASA Astrophysics Data System (ADS)

Pequito, Sérgio; Preciado, Victor M.; Barabási, Albert-László; Pappas, George J.

2017-01-01

Recent advances in control theory provide us with efficient tools to determine the minimum number of driving (or driven) nodes to steer a complex network towards a desired state. Furthermore, we often need to do it within a given time window, so it is of practical importance to understand the trade-offs between the minimum number of driving/driven nodes and the minimum time required to reach a desired state. Therefore, we introduce the notion of actuation spectrum to capture such trade-offs, which we used to find that in many complex networks only a small fraction of driving (or driven) nodes is required to steer the network to a desired state within a relatively small time window. Furthermore, our empirical studies reveal that, even though synthetic network models are designed to present structural properties similar to those observed in real networks, their actuation spectra can be dramatically different. Thus, it supports the need to develop new synthetic network models able to replicate controllability properties of real-world networks.

Real Time Data for Seismology at the IRIS Data Management Center, AN Nsf-Sponsored Facility

NASA Astrophysics Data System (ADS)

Benson, R. B.; Ahern, T. K.; Trabant, C.; Weertman, B. R.; Casey, R.; Stromme, S.; Karstens, R.

2012-12-01

When IRIS was incorporated in 1984, it committed to provide long-term support for the science of seismology. It first upgraded analog networks by installing observatory grade digital seismic recording equipment (by constructing the Global Seismic Network to upgrade the World Wide Standardized Seismographic Network) that became the backbone of the International Federation of Digital Seismic Networks (FDSN), and in 1990 constructed a state-of-the-art data center that would allow free and open access to data to everyone. For the first decade, IRIS leveraged a complicated system of telemetry which laid the foundation for delivering (relatively) high rate and continuous seismic time series data to the IRIS Data Management Center, which was designed to accept data that arrived with highly variable latencies and on many media formats. This meant that science had to often wait until data became complete, which at the time was primarily related to studying earthquakes or similar events. During the 1990's, numerous incremental but small improvements were made to get data into the hands of users with less latency, leveraging dialup, satellite telemetry, and a variety of Internet protocols. But beginning in 2000, the IRIS Data Management Center began the process of accumulating data comprehensively in real time. It was first justified because it eliminated the time-consuming transcription and manual data handling on various media formats, like magnetic tapes, CD's and DVD's. However, the switch to real-time telemetry proved to be a major improvement technologically because it not only simplified data transfer, it opened access to a large volume of previously inaccessible data (local resource limitations), and many networks began willingly providing their geophysical data to the broad research community. It also enabled researchers the ability to process data in different and streamlined ways, by incorporating data directly into workflows and processing packages. Any network on the Internet, small or large, can now share data, and today, the IRIS DMC receives nearly all of its seismic data from regional and international networks in real time. We will show that this evolution to managing real time data has provided the framework for accomplishing many important benefits that illustrate that open, real time data should be the goal of every observatory operation and can provide: - Faster (therefore cost and data saving) quality control, - Data products that highlight source properties and provide teachable moments - Data delivery to regional or national networks around the globe for immediate access for monitoring. -Use in teaching the public, providing streaming data to museums, schools, etc.

CyberPetri at CDX 2016: Real-time Network Situation Awareness

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

Arendt, Dustin L.; Best, Daniel M.; Burtner, Edwin R.

CyberPetri is a novel visualization technique that provides a flexible map of the network based on available characteristics, such as IP address, operating system, or service. Previous work introduced CyberPetri as a visualization feature in Ocelot, a network defense tool that helped security analysts understand and respond to an active defense scenario. In this paper we present a case study in which we use the CyberPetri visualization technique to support real-time situation awareness during the 2016 Cyber Defense Exercise.

Results of an Internet-Based Dual-Frequency Global Differential GPS System

NASA Technical Reports Server (NTRS)

Muellerschoen, R.; Bertiger, W.; Lough, M.

2000-01-01

Observables from a global network of 18 GPS receivers are returned in real-time to JPL over the open Internet. 30 - 40 cm RSS global GPS orbits and precise dual-frequency GPS clocks are computed in real-time with JPL's Real Time Gipsy (RTG) software.

Modeling and performance analysis using extended fuzzy-timing Petri nets for networked virtual environments.

PubMed

Zhou, Y; Murata, T; Defanti, T A

2000-01-01

Despite their attractive properties, networked virtual environments (net-VEs) are notoriously difficult to design, implement, and test due to the concurrency, real-time and networking features in these systems. Net-VEs demand high quality-of-service (QoS) requirements on the network to maintain natural and real-time interactions among users. The current practice for net-VE design is basically trial and error, empirical, and totally lacks formal methods. This paper proposes to apply a Petri net formal modeling technique to a net-VE-NICE (narrative immersive constructionist/collaborative environment), predict the net-VE performance based on simulation, and improve the net-VE performance. NICE is essentially a network of collaborative virtual reality systems called the CAVE-(CAVE automatic virtual environment). First, we introduce extended fuzzy-timing Petri net (EFTN) modeling and analysis techniques. Then, we present EFTN models of the CAVE, NICE, and transport layer protocol used in NICE: transmission control protocol (TCP). We show the possibility analysis based on the EFTN model for the CAVE. Then, by using these models and design/CPN as the simulation tool, we conducted various simulations to study real-time behavior, network effects and performance (latencies and jitters) of NICE. Our simulation results are consistent with experimental data.

UMTS rapid response real-time seismic networks: implementation and strategies at INGV

NASA Astrophysics Data System (ADS)

Govoni, Aladino; Margheriti, Lucia; Moretti, Milena; Lauciani, Valentino; Sensale, Gianpaolo; Bucci, Augusto; Criscuoli, Fabio

2015-04-01

The benefits of portable real-time seismic networks are several and well known. During the management of a temporary experiment from the real-time data it is possible to detect and fix rapidly problems with power supply, time synchronization, disk failures and, most important, seismic signal quality degradation due to unexpected noise sources or sensor alignment/tampering. This usually minimizes field maintenance trips and maximizes both the quantity and the quality of the acquired data. When the area of the temporary experiment is not well monitored by the local permanent network, the real-time data from the temporary experiment can be fed to the permanent network monitoring system improving greatly both the real-time hypocentral locations and the final revised bulletin. All these benefits apply also in case of seismic crises when rapid deployment stations can significantly contribute to the aftershock analysis. Nowadays data transmission using meshed radio networks or satellite systems is not a big technological problem for a permanent seismic network where each site is optimized for the device power consumption and is usually installed by properly specialized technicians that can configure transmission devices and align antennas. This is not usually practical for temporary networks and especially for rapid response networks where the installation time is the main concern. These difficulties are substantially lowered using the now widespread UMTS technology for data transmission. A small (but sometimes power hungry) properly configured device with an omnidirectional antenna must be added to the station assembly. All setups are usually configured before deployment and this allows for an easy installation also by untrained personnel. We describe here the implementation of a UMTS based portable seismic network for both temporary experiments and rapid response applications developed at INGV. The first field experimentation of this approach dates back to the 2009 L'Aquila aftershock sequence and since then it has been customized and refined to overcome most reliability and security issues using an industry standard VPN architecture that allows to avoid UMTS provider firewall problems and does not expose to the Internet the usually weak and attack prone data acquisition ports. With this approach all the devices are protected inside a local network and the only exposed port is the VPN server one. This solution improves both the security and the bandwidth available to data transmission. While most of the experimentation has been carried out using the RefTek units of the INGV Mobile Network this solution applies equally well to most seismic data loggers available on the market. Overall the UMTS data transmission has been used in most temporary seismic experiments and in all seismic emergencies happened in Italy since 2010 and has proved to be a very cost effective approach with real-time data acquisition rates usually greater than 97% and all the benefits that result from the fast integration of the temporary data in the National Network monitoring system and in the EIDA data bank.

Implementation of the Disruption Predictor APODIS in JET's Real-Time Network Using the MARTe Framework

NASA Astrophysics Data System (ADS)

López, Juan Manuel; Vega, J.; Alves, D.; Dormido-Canto, S.; Murari, A.; Ramírez, J. M.; Felton, R.; Ruiz, M.; de Arcas, G.

2014-04-01

This paper describes the implementation of a real-time disruption predictor that is based on support vector machine (SVM) classifiers. The implementation was performed under the MARTe framework on a six-core x86 architecture. The system is connected via JET's Real-time Data Network (RTDN). The online results show a high degree of successful predictions and a low rate of false alarms, thus confirming the usefulness of this approach in a disruption mitigation scheme. The implementation shows a low computational load, which will be exploited in the immediate future to increase the prediction's temporal resolution.

Field test of a practical secure communication network with decoy-state quantum cryptography.

PubMed

Chen, Teng-Yun; Liang, Hao; Liu, Yang; Cai, Wen-Qi; Ju, Lei; Liu, Wei-Yue; Wang, Jian; Yin, Hao; Chen, Kai; Chen, Zeng-Bing; Peng, Cheng-Zhi; Pan, Jian-Wei

2009-04-13

We present a secure network communication system that operated with decoy-state quantum cryptography in a real-world application scenario. The full key exchange and application protocols were performed in real time among three nodes, in which two adjacent nodes were connected by approximate 20 km of commercial telecom optical fiber. The generated quantum keys were immediately employed and demonstrated for communication applications, including unbreakable real-time voice telephone between any two of the three communication nodes, or a broadcast from one node to the other two nodes by using one-time pad encryption.

Architectural impact of FDDI network on scheduling hard real-time traffic

NASA Technical Reports Server (NTRS)

Agrawal, Gopal; Chen, Baio; Zhao, Wei; Davari, Sadegh

1991-01-01

The architectural impact on guaranteeing synchronous message deadlines in FDDI (Fiber Distributed Data Interface) token ring networks is examined. The FDDI network does not have facility to support (global) priority arbitration which is a useful facility for scheduling hard real time activities. As a result, it was found that the worst case utilization of synchronous traffic in an FDDI network can be far less than that in a centralized single processor system. Nevertheless, it is proposed and analyzed that a scheduling method can guarantee deadlines of synchronous messages having traffic utilization up to 33 pct., the highest to date.

Application of neural network for real-time measurement of electrical resistivity in cold crucible

NASA Astrophysics Data System (ADS)

Votava, Pavel; Poznyak, Igor

2017-08-01

The article describes use of an Induction furnace with cold crucible as a tool for real-time measurement of a melted material electrical resistivity. The measurement is based on an inverse problem solution of a 2D mathematical model, possibly implementable in a microcontroller or a FPGA in a form of a neural network. The 2D mathematical model results has been provided as a training set for the neural network. At the end, the implementation results are discussed together with uncertainty of measurement, which is done by the neural network implementation itself.

Wavelets and Elman Neural Networks for monitoring environmental variables

NASA Astrophysics Data System (ADS)

Ciarlini, Patrizia; Maniscalco, Umberto

2008-11-01

An application in cultural heritage is introduced. Wavelet decomposition and Neural Networks like virtual sensors are jointly used to simulate physical and chemical measurements in specific locations of a monument. Virtual sensors, suitably trained and tested, can substitute real sensors in monitoring the monument surface quality, while the real ones should be installed for a long time and at high costs. The application of the wavelet decomposition to the environmental data series allows getting the treatment of underlying temporal structure at low frequencies. Consequently a separate training of suitable Elman Neural Networks for high/low components can be performed, thus improving the networks convergence in learning time and measurement accuracy in working time.

Satellite clock corrections estimation to accomplish real time ppp: experiments for brazilian real time network

NASA Astrophysics Data System (ADS)

Marques, Haroldo; Monico, João; Aquino, Marcio; Melo, Weyller

2014-05-01

The real time PPP method requires the availability of real time precise orbits and satellites clocks corrections. Currently, it is possible to apply the solutions of clocks and orbits available by BKG within the context of IGS Pilot project or by using the operational predicted IGU ephemeris. The accuracy of the satellite position available in the IGU is enough for several applications requiring good quality. However, the satellites clocks corrections do not provide enough accuracy (3 ns ~ 0.9 m) to accomplish real time PPP with the same level of accuracy. Therefore, for real time PPP application it is necessary to further research and develop appropriated methodologies for estimating the satellite clock corrections in real time with better accuracy. Currently, it is possible to apply the real time solutions of clocks and orbits available by Federal Agency for Cartography and Geodesy (BKG) within the context of IGS Pilot project. The BKG corrections are disseminated by a new proposed format of the RTCM 3.x and can be applied in the broadcasted orbits and clocks. Some investigations have been proposed for the estimation of the satellite clock corrections using GNSS code and phase observable at the double difference level between satellites and epochs (MERVAT, DOUSA, 2007). Another possibility consists of applying a Kalman Filter in the PPP network mode (HAUSCHILD, 2010) and it is also possible the integration of both methods, using network PPP and observables at double difference level in specific time intervals (ZHANG; LI; GUO, 2010). For this work the methodology adopted consists in the estimation of the satellite clock corrections based on the data adjustment in the PPP mode, but for a network of GNSS stations. The clock solution can be solved by using two types of observables: code smoothed by carrier phase or undifferenced code together with carrier phase. In the former, we estimate receiver clock error; satellite clock correction and troposphere, considering that the phase ambiguities are eliminated when applying differences between consecutive epochs. However, when using undifferenced code and phase, the ambiguities may be estimated together with receiver clock errors, satellite clock corrections and troposphere parameters. In both strategies it is also possible to correct the troposphere delay from a Numerical Weather Forecast Model instead of estimating it. The prediction of the satellite clock correction can be performed using a straight line or a second degree polynomial using the time series of the estimated satellites clocks. To estimate satellite clock correction and to accomplish real time PPP two pieces of software have been developed, respectively, "RT_PPP" and "RT_SAT_CLOCK". The system (RT_PPP) is able to process GNSS code and phase data using precise ephemeris and precise satellites clocks corrections together with several corrections required for PPP. In the software RT_SAT_CLOCK we apply a Kalman filter algorithm to estimate satellite clock correction in the network PPP mode. In this case, all PPP corrections must be applied for each station. The experiments were generated in real time and post-processed mode (simulating real time) considering data from the Brazilian continuous GPS network and also from the IGS network in a global satellite clock solution. We have used IGU ephemeris for satellite position and estimated the satellite clock corrections, performing the updates as soon as new ephemeris files were available. Experiments were accomplished in order to assess the accuracy of the estimated clocks when using the Brazilian Numerical Weather Forecast Model (BNWFM) from CPTEC/INPE and also using the ZTD from European Centre for Medium-Range Weather Forecasts (ECMWF) together with Vienna Mapping Function VMF or estimating troposphere with clocks and ambiguities in the Kalman Filter. The daily precision of the estimated satellite clock corrections reached the order of 0.15 nanoseconds. The clocks were applied in the Real Time PPP for Brazilian network stations and also for flight test of the Brazilian airplanes and the results show that it is possible to accomplish real time PPP in the static and kinematic modes with accuracy of the order of 10 to 20 cm, respectively.

Enhancing the Quality of Service for Real Time Traffic over Optical Burst Switching (OBS) Networks with Ensuring the Fairness for Other Traffics

PubMed Central

Al-Shargabi, Mohammed A.; Ismail, Abdulsamad S.

2016-01-01

Optical burst switching (OBS) networks have been attracting much consideration as a promising approach to build the next generation optical Internet. A solution for enhancing the Quality of Service (QoS) for high priority real time traffic over OBS with the fairness among the traffic types is absent in current OBS’ QoS schemes. In this paper we present a novel Real Time Quality of Service with Fairness Ratio (RT-QoSFR) scheme that can adapt the burst assembly parameters according to the traffic QoS needs in order to enhance the real time traffic QoS requirements and to ensure the fairness for other traffic. The results show that RT-QoSFR scheme is able to fulfill the real time traffic requirements (end to end delay, and loss rate) ensuring the fairness for other traffics under various conditions such as the type of real time traffic and traffic load. RT-QoSFR can guarantee that the delay of the real time traffic packets does not exceed the maximum packets transfer delay value. Furthermore, it can reduce the real time traffic packets loss, at the same time guarantee the fairness for non real time traffic packets by determining the ratio of real time traffic inside the burst to be 50–60%, 30–40%, and 10–20% for high, normal, and low traffic loads respectively. PMID:27583557

Revolutionize Situational Awareness in Emergencies

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

Hehlen, Markus Peter

This report describes an integrated system that provides real-time actionable information to first responders. LANL will integrate three technologies to form an advanced predictive real-time sensor network including compact chemical and wind sensor sin low cost rugged package for outdoor installation; flexible robust communication architecture linking sensors in near-real time to globally accessible servers; and the QUIC code which predicts contamination transport and dispersal in urban environments in near real time.

From calls to communities: a model for time-varying social networks

NASA Astrophysics Data System (ADS)

Laurent, Guillaume; Saramäki, Jari; Karsai, Márton

2015-11-01

Social interactions vary in time and appear to be driven by intrinsic mechanisms that shape the emergent structure of social networks. Large-scale empirical observations of social interaction structure have become possible only recently, and modelling their dynamics is an actual challenge. Here we propose a temporal network model which builds on the framework of activity-driven time-varying networks with memory. The model integrates key mechanisms that drive the formation of social ties - social reinforcement, focal closure and cyclic closure, which have been shown to give rise to community structure and small-world connectedness in social networks. We compare the proposed model with a real-world time-varying network of mobile phone communication, and show that they share several characteristics from heterogeneous degrees and weights to rich community structure. Further, the strong and weak ties that emerge from the model follow similar weight-topology correlations as real-world social networks, including the role of weak ties.

Real-Time Earthquake Analysis for Disaster Mitigation (READI) Network

NASA Astrophysics Data System (ADS)

Bock, Y.

2014-12-01

Real-time GNSS networks are making a significant impact on our ability to forecast, assess, and mitigate the effects of geological hazards. I describe the activities of the Real-time Earthquake Analysis for Disaster Mitigation (READI) working group. The group leverages 600+ real-time GPS stations in western North America operated by UNAVCO (PBO network), Central Washington University (PANGA), US Geological Survey & Scripps Institution of Oceanography (SCIGN project), UC Berkeley & US Geological Survey (BARD network), and the Pacific Geosciences Centre (WCDA project). Our goal is to demonstrate an earthquake and tsunami early warning system for western North America. Rapid response is particularly important for those coastal communities that are in the near-source region of large earthquakes and may have only minutes of warning time, and who today are not adequately covered by existing seismic and basin-wide ocean-buoy monitoring systems. The READI working group is performing comparisons of independent real time analyses of 1 Hz GPS data for station displacements and is participating in government-sponsored earthquake and tsunami exercises in the Western U.S. I describe a prototype seismogeodetic system using a cluster of southern California stations that includes GNSS tracking and collocation with MEMS accelerometers for real-time estimation of seismic velocity and displacement waveforms, which has advantages for improved earthquake early warning and tsunami forecasts compared to seismic-only or GPS-only methods. The READI working group's ultimate goal is to participate in an Indo-Pacific Tsunami early warning system that utilizes GNSS real-time displacements and ionospheric measurements along with seismic, near-shore buoys and ocean-bottom pressure sensors, where available, to rapidly estimate magnitude and finite fault slip models for large earthquakes, and then forecast tsunami source, energy scale, geographic extent, inundation and runup. This will require cooperation with other real-time efforts around the Pacific Rim in terms of sharing, analysis centers, and advisory bulletins to the responsible government agencies. The IAG's Global Geodetic Observing System (GGOS), in particular its natural hazards theme, provides a natural umbrella for achieving this objective.

A Physics-driven Neural Networks-based Simulation System (PhyNNeSS) for multimodal interactive virtual environments involving nonlinear deformable objects

PubMed Central

De, Suvranu; Deo, Dhannanjay; Sankaranarayanan, Ganesh; Arikatla, Venkata S.

2012-01-01

Background While an update rate of 30 Hz is considered adequate for real time graphics, a much higher update rate of about 1 kHz is necessary for haptics. Physics-based modeling of deformable objects, especially when large nonlinear deformations and complex nonlinear material properties are involved, at these very high rates is one of the most challenging tasks in the development of real time simulation systems. While some specialized solutions exist, there is no general solution for arbitrary nonlinearities. Methods In this work we present PhyNNeSS - a Physics-driven Neural Networks-based Simulation System - to address this long-standing technical challenge. The first step is an off-line pre-computation step in which a database is generated by applying carefully prescribed displacements to each node of the finite element models of the deformable objects. In the next step, the data is condensed into a set of coefficients describing neurons of a Radial Basis Function network (RBFN). During real-time computation, these neural networks are used to reconstruct the deformation fields as well as the interaction forces. Results We present realistic simulation examples from interactive surgical simulation with real time force feedback. As an example, we have developed a deformable human stomach model and a Penrose-drain model used in the Fundamentals of Laparoscopic Surgery (FLS) training tool box. Conclusions A unique computational modeling system has been developed that is capable of simulating the response of nonlinear deformable objects in real time. The method distinguishes itself from previous efforts in that a systematic physics-based pre-computational step allows training of neural networks which may be used in real time simulations. We show, through careful error analysis, that the scheme is scalable, with the accuracy being controlled by the number of neurons used in the simulation. PhyNNeSS has been integrated into SoFMIS (Software Framework for Multimodal Interactive Simulation) for general use. PMID:22629108

Flying Real-Time Network to Coordinate Disaster Relief Activities in Urban Areas †

PubMed Central

Micheletto, Matias; Orozco, Javier; Mosse, Daniel

2018-01-01

While there have been important advances within wireless communication technology, the provision of communication support during disaster relief activities remains an open issue. The literature in disaster research reports several major restrictions to conducting first response activities in urban areas, given the limitations of telephone networks and radio systems to provide digital communication in the field. In search-and-rescue operations, the communication requirements are increased, since the first responders need to rely on real-time and reliable communication to perform their activities and coordinate their efforts with other teams. Therefore, these limitations open the door to improvisation during disaster relief efforts. In this paper, we argue that flying ad-hoc networks can provide the communication support needed in these scenarios, and propose a new solution towards that goal. The proposal involves the use of flying witness units, implemented using drones, that act as communication gateways between first responders working at different locations of the affected area. The proposal is named the Flying Real-Time Network, and its feasibility to provide communication in a disaster scenario is shown by presenting both a real-time schedulability analysis of message delivery, as well as simulations of the communication support in a physical scenario inspired by a real incident. The obtained results were highly positive and consistent, therefore this proposal represents a step forward towards the solution of this open issue. PMID:29789458

Flying Real-Time Network to Coordinate Disaster Relief Activities in Urban Areas †.

PubMed

Micheletto, Matias; Petrucci, Vinicius; Santos, Rodrigo; Orozco, Javier; Mosse, Daniel; Ochoa, Sergio F; Meseguer, Roc

2018-05-22

While there have been important advances within wireless communication technology, the provision of communication support during disaster relief activities remains an open issue. The literature in disaster research reports several major restrictions to conducting first response activities in urban areas, given the limitations of telephone networks and radio systems to provide digital communication in the field. In search-and-rescue operations, the communication requirements are increased, since the first responders need to rely on real-time and reliable communication to perform their activities and coordinate their efforts with other teams. Therefore, these limitations open the door to improvisation during disaster relief efforts. In this paper, we argue that flying ad-hoc networks can provide the communication support needed in these scenarios, and propose a new solution towards that goal. The proposal involves the use of flying witness units, implemented using drones, that act as communication gateways between first responders working at different locations of the affected area. The proposal is named the Flying Real-Time Network, and its feasibility to provide communication in a disaster scenario is shown by presenting both a real-time schedulability analysis of message delivery, as well as simulations of the communication support in a physical scenario inspired by a real incident. The obtained results were highly positive and consistent, therefore this proposal represents a step forward towards the solution of this open issue.

A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications

PubMed Central

Slama, Matous; Benes, Peter M.; Bila, Jiri

2015-01-01

During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several hundred milliseconds for repositioning the radiation beam also affects the accuracy. In order to compensate the latency, neural network prediction technique with real-time retraining can be used. We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency. The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms. The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time. For one-second prediction horizon, the proposed techniques achieved accuracy less than one millimeter of 3D mean absolute error in one hundred seconds of total treatment time. PMID:25893194

A fast neural network approach to predict lung tumor motion during respiration for radiation therapy applications.

PubMed

Bukovsky, Ivo; Homma, Noriyasu; Ichiji, Kei; Cejnek, Matous; Slama, Matous; Benes, Peter M; Bila, Jiri

2015-01-01

During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several hundred milliseconds for repositioning the radiation beam also affects the accuracy. In order to compensate the latency, neural network prediction technique with real-time retraining can be used. We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency. The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms. The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time. For one-second prediction horizon, the proposed techniques achieved accuracy less than one millimeter of 3D mean absolute error in one hundred seconds of total treatment time.

Meeting the Challenge of Distributed Real-Time & Embedded (DRE) Systems

DTIC Science & Technology

2012-05-10

IP RTOS Middleware Middleware Services DRE Applications Operating Sys & Protocols Hardware & Networks Middleware Middleware Services DRE...Services COTS & standards-based middleware, language, OS , network, & hardware platforms • Real-time CORBA (TAO) middleware • ADAPTIVE Communication...SPLs) F-15 product variant A/V 8-B product variant F/A 18 product variant UCAV product variant Software Produce-Line Hardware (CPU, Memory, I/O) OS

Teacher Directed Design: Content Knowledge, Pedagogy and Assessment under the Nevada K-12 Real-Time Seismic Network

NASA Astrophysics Data System (ADS)

Cantrell, P.; Ewing-Taylor, J.; Crippen, K. J.; Smith, K. D.; Snelson, C. M.

2004-12-01

Education professionals and seismologists under the emerging SUN (Shaking Up Nevada) program are leveraging the existing infrastructure of the real-time Nevada K-12 Seismic Network to provide a unique inquiry based science experience for teachers. The concept and effort are driven by teacher needs and emphasize rigorous content knowledge acquisition coupled with the translation of that knowledge into an integrated seismology based earth sciences curriculum development process. We are developing a pedagogical framework, graduate level coursework, and materials to initiate the SUN model for teacher professional development in an effort to integrate the research benefits of real-time seismic data with science education needs in Nevada. A component of SUN is to evaluate teacher acquisition of qualified seismological and earth science information and pedagogy both in workshops and in the classroom and to assess the impact on student achievement. SUN's mission is to positively impact earth science education practices. With the upcoming EarthScope initiative, the program is timely and will incorporate EarthScope real-time seismic data (USArray) and educational materials in graduate course materials and teacher development programs. A number of schools in Nevada are contributing real-time data from both inexpensive and high-quality seismographs that are integrated with Nevada regional seismic network operations as well as the IRIS DMC. A powerful and unique component of the Nevada technology model is that schools can receive "stable" continuous live data feeds from 100's seismograph stations in Nevada, California and world (including live data from Earthworm systems and the IRIS DMC BUD - Buffer of Uniform Data). Students and teachers see their own networked seismograph station within a global context, as participants in regional and global monitoring. The robust real-time Internet communications protocols invoked in the Nevada network provide for local data acquisition, remote multi-channel data access, local time-series data management, interactive multi-window waveform display and time-series analysis with centralized meta-data control. Formally integrating educational seismology into the K-12 science curriculum with an overall "positive" impact to science education practices necessarily requires a collaborative effort between professional educators and seismologists yet driven exclusively by teacher needs.

Real-time monitoring implementation in a remote-pumped WDM PON

NASA Astrophysics Data System (ADS)

Liaw, S.-K.; Hong, K.-L.; Shei, Y.-S.

2008-08-01

We report on an improved configuration to monitor a passive optical network with high quality in service. This proposed system comprises fiber-Bragg gratings, a 1 × 4 optical switch, and an optical time-domain reflectometry to diagnose the broken point in real time. It could simultaneously detect multioptical network units in a WDM PON. The remote-pump integrated residual pumping reused function is implemented. Broken points in different optical paths can be detected simultaneously even when the distances to the central office are identical. The bit-error rate testing is verified with a small power penalty, making it an ideal solution for the real-time monitoring in a WDM PON.

Remote observatory access via the Advanced Communications Technology Satellite

NASA Technical Reports Server (NTRS)

Horan, Stephen; Anderson, Kurt; Georghiou, Georghios

1992-01-01

An investigation of the potential for using the ACTS to provide the data distribution network for a distributed set of users of an astronomical observatory has been conducted. The investigation consisted of gathering the data and interface standards for the ACTS network and the observatory instrumentation and telecommunications devices. A simulation based on COMNET was then developed to test data transport configurations for real-time suitability. The investigation showed that the ACTS network should support the real-time requirements and allow for growth in the observatory needs for data transport.

Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model

PubMed Central

van Albada, Sacha J.; Rowley, Andrew G.; Senk, Johanna; Hopkins, Michael; Schmidt, Maximilian; Stokes, Alan B.; Lester, David R.; Diesmann, Markus; Furber, Steve B.

2018-01-01

The digital neuromorphic hardware SpiNNaker has been developed with the aim of enabling large-scale neural network simulations in real time and with low power consumption. Real-time performance is achieved with 1 ms integration time steps, and thus applies to neural networks for which faster time scales of the dynamics can be neglected. By slowing down the simulation, shorter integration time steps and hence faster time scales, which are often biologically relevant, can be incorporated. We here describe the first full-scale simulations of a cortical microcircuit with biological time scales on SpiNNaker. Since about half the synapses onto the neurons arise within the microcircuit, larger cortical circuits have only moderately more synapses per neuron. Therefore, the full-scale microcircuit paves the way for simulating cortical circuits of arbitrary size. With approximately 80, 000 neurons and 0.3 billion synapses, this model is the largest simulated on SpiNNaker to date. The scale-up is enabled by recent developments in the SpiNNaker software stack that allow simulations to be spread across multiple boards. Comparison with simulations using the NEST software on a high-performance cluster shows that both simulators can reach a similar accuracy, despite the fixed-point arithmetic of SpiNNaker, demonstrating the usability of SpiNNaker for computational neuroscience applications with biological time scales and large network size. The runtime and power consumption are also assessed for both simulators on the example of the cortical microcircuit model. To obtain an accuracy similar to that of NEST with 0.1 ms time steps, SpiNNaker requires a slowdown factor of around 20 compared to real time. The runtime for NEST saturates around 3 times real time using hybrid parallelization with MPI and multi-threading. However, achieving this runtime comes at the cost of increased power and energy consumption. The lowest total energy consumption for NEST is reached at around 144 parallel threads and 4.6 times slowdown. At this setting, NEST and SpiNNaker have a comparable energy consumption per synaptic event. Our results widen the application domain of SpiNNaker and help guide its development, showing that further optimizations such as synapse-centric network representation are necessary to enable real-time simulation of large biological neural networks. PMID:29875620

A neural network architecture for implementation of expert systems for real time monitoring

NASA Technical Reports Server (NTRS)

Ramamoorthy, P. A.

1991-01-01

Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered.

Memory Network For Distributed Data Processors

NASA Technical Reports Server (NTRS)

Bolen, David; Jensen, Dean; Millard, ED; Robinson, Dave; Scanlon, George

1992-01-01

Universal Memory Network (UMN) is modular, digital data-communication system enabling computers with differing bus architectures to share 32-bit-wide data between locations up to 3 km apart with less than one millisecond of latency. Makes it possible to design sophisticated real-time and near-real-time data-processing systems without data-transfer "bottlenecks". This enterprise network permits transmission of volume of data equivalent to an encyclopedia each second. Facilities benefiting from Universal Memory Network include telemetry stations, simulation facilities, power-plants, and large laboratories or any facility sharing very large volumes of data. Main hub of UMN is reflection center including smaller hubs called Shared Memory Interfaces.

Cluster-Based Multipolling Sequencing Algorithm for Collecting RFID Data in Wireless LANs

NASA Astrophysics Data System (ADS)

Choi, Woo-Yong; Chatterjee, Mainak

2015-03-01

With the growing use of RFID (Radio Frequency Identification), it is becoming important to devise ways to read RFID tags in real time. Access points (APs) of IEEE 802.11-based wireless Local Area Networks (LANs) are being integrated with RFID networks that can efficiently collect real-time RFID data. Several schemes, such as multipolling methods based on the dynamic search algorithm and random sequencing, have been proposed. However, as the number of RFID readers associated with an AP increases, it becomes difficult for the dynamic search algorithm to derive the multipolling sequence in real time. Though multipolling methods can eliminate the polling overhead, we still need to enhance the performance of the multipolling methods based on random sequencing. To that extent, we propose a real-time cluster-based multipolling sequencing algorithm that drastically eliminates more than 90% of the polling overhead, particularly so when the dynamic search algorithm fails to derive the multipolling sequence in real time.

Improving the Capture and Re-Use of Data with Wearable Computers

NASA Technical Reports Server (NTRS)

Pfarr, Barbara; Fating, Curtis C.; Green, Daniel; Powers, Edward I. (Technical Monitor)

2001-01-01

At the Goddard Space Flight Center, members of the Real-Time Software Engineering Branch are developing a wearable, wireless, voice-activated computer for use in a wide range of crosscutting space applications that would benefit from having instant Internet, network, and computer access with complete mobility and hands-free operations. These applications can be applied across many fields and disciplines including spacecraft fabrication, integration and testing (including environmental testing), and astronaut on-orbit control and monitoring of experiments with ground based experimenters. To satisfy the needs of NASA customers, this wearable computer needs to be connected to a wireless network, to transmit and receive real-time video over the network, and to receive updated documents via the Internet or NASA servers. The voice-activated computer, with a unique vocabulary, will allow the users to access documentation in a hands free environment and interact in real-time with remote users. We will discuss wearable computer development, hardware and software issues, wireless network limitations, video/audio solutions and difficulties in language development.

Real-Time Tropospheric Delay Estimation using IGS Products

NASA Astrophysics Data System (ADS)

Stürze, Andrea; Liu, Sha; Söhne, Wolfgang

2014-05-01

The Federal Agency for Cartography and Geodesy (BKG) routinely provides zenith tropospheric delay (ZTD) parameter for the assimilation in numerical weather models since more than 10 years. Up to now the results flowing into the EUREF Permanent Network (EPN) or E-GVAP (EUMETNET EIG GNSS water vapour programme) analysis are based on batch processing of GPS+GLONASS observations in differential network mode. For the recently started COST Action ES1206 about "Advanced Global Navigation Satellite Systems tropospheric products for monitoring severe weather events and climate" (GNSS4SWEC), however, rapid updates in the analysis of the atmospheric state for nowcasting applications require changing the processing strategy towards real-time. In the RTCM SC104 (Radio Technical Commission for Maritime Services, Special Committee 104) a format combining the advantages of Precise Point Positioning (PPP) and Real-Time Kinematic (RTK) is under development. The so-called State Space Representation approach is defining corrections, which will be transferred in real-time to the user e.g. via NTRIP (Network Transport of RTCM via Internet Protocol). Meanwhile messages for precise orbits, satellite clocks and code biases compatible to the basic PPP mode using IGS products are defined. Consequently, the IGS Real-Time Service (RTS) was launched in 2013 in order to extend the well-known precise orbit and clock products by a real-time component. Further messages e.g. with respect to ionosphere or phase biases are foreseen. Depending on the level of refinement, so different accuracies up to the RTK level shall be reachable. In co-operation of BKG and the Technical University of Darmstadt the real-time software GEMon (GREF EUREF Monitoring) is under development. GEMon is able to process GPS and GLONASS observation and RTS product data streams in PPP mode. Furthermore, several state-of-the-art troposphere models, for example based on numerical weather prediction data, are implemented. Hence, it opens the possibility to evaluate the potential of troposphere parameter determination in real-time and its effect to Precise Point Positioning. Starting with an offline investigation of the influence of different RTS products and a priori troposphere models the configuration delivering the best results is used for a real-time processing of the GREF (German Geodetic Reference) network over a suitable period of time. The evaluation of the derived ZTD parameters and station heights is done with respect to well proven GREF, EUREF, IGS, and E-GVAP analysis results. Keywords: GNSS, Zenith Tropospheric Delay, Real-time Precise Point Positioning

Development of inferential sensors for real-time quality control of water-level data for the Everglades Depth Estimation Network

USGS Publications Warehouse

Daamen, Ruby C.; Edwin A. Roehl, Jr.; Conrads, Paul

2010-01-01

A technology often used for industrial applications is “inferential sensor.” Rather than installing a redundant sensor to measure a process, such as an additional waterlevel gage, an inferential sensor, or virtual sensor, is developed that estimates the processes measured by the physical sensor. The advantage of an inferential sensor is that it provides a redundant signal to the sensor in the field but without exposure to environmental threats. In the event that a gage does malfunction, the inferential sensor provides an estimate for the period of missing data. The inferential sensor also can be used in the quality assurance and quality control of the data. Inferential sensors for gages in the EDEN network are currently (2010) under development. The inferential sensors will be automated so that the real-time EDEN data will continuously be compared to the inferential sensor signal and digital reports of the status of the real-time data will be sent periodically to the appropriate support personnel. The development and application of inferential sensors is easily transferable to other real-time hydrologic monitoring networks.

U.S. Geological Survey Real-Time River Data Applications

USGS Publications Warehouse

Morlock, Scott E.

1998-01-01

Real-time river data provided by the USGS originate from streamflow-gaging stations. The USGS operates and maintains a network of more than 7,000 such stations across the nation (Mason and Wieger, 1995). These gaging stations, used to produce records of stage and streamflow data, are operated in cooperation with local, state, and other federal agencies. The USGS office in Indianapolis operates a statewide network of more than 170 gaging stations. The instrumentation at USGS gaging stations monitors and records river information, primarily river stage (fig. 1). As technological advances are made, many USGS gaging stations are being retrofitted with electronic instrumentation to monitor and record river data. Electronic instrumentation facilitates transmission of real-time or near real-time river data for use by government agencies in such flood-related tasks as operating flood-control structures and ordering evacuations.

A Tree Based Broadcast Scheme for (m, k)-firm Real-Time Stream in Wireless Sensor Networks.

PubMed

Park, HoSung; Kim, Beom-Su; Kim, Kyong Hoon; Shah, Babar; Kim, Ki-Il

2017-11-09

Recently, various unicast routing protocols have been proposed to deliver measured data from the sensor node to the sink node within the predetermined deadline in wireless sensor networks. In parallel with their approaches, some applications demand the specific service, which is based on broadcast to all nodes within the deadline, the feasible real-time traffic model and improvements in energy efficiency. However, current protocols based on either flooding or one-to-one unicast cannot meet the above requirements entirely. Moreover, as far as the authors know, there is no study for the real-time broadcast protocol to support the application-specific traffic model in WSN yet. Based on the above analysis, in this paper, we propose a new ( m , k )-firm-based Real-time Broadcast Protocol (FRBP) by constructing a broadcast tree to satisfy the ( m , k )-firm, which is applicable to the real-time model in resource-constrained WSNs. The broadcast tree in FRBP is constructed by the distance-based priority scheme, whereas energy efficiency is improved by selecting as few as nodes on a tree possible. To overcome the unstable network environment, the recovery scheme invokes rapid partial tree reconstruction in order to designate another node as the parent on a tree according to the measured ( m , k )-firm real-time condition and local states monitoring. Finally, simulation results are given to demonstrate the superiority of FRBP compared to the existing schemes in terms of average deadline missing ratio, average throughput and energy consumption.

PAVENET OS: A Compact Hard Real-Time Operating System for Precise Sampling in Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Saruwatari, Shunsuke; Suzuki, Makoto; Morikawa, Hiroyuki

The paper shows a compact hard real-time operating system for wireless sensor nodes called PAVENET OS. PAVENET OS provides hybrid multithreading: preemptive multithreading and cooperative multithreading. Both of the multithreading are optimized for two kinds of tasks on wireless sensor networks, and those are real-time tasks and best-effort ones. PAVENET OS can efficiently perform hard real-time tasks that cannot be performed by TinyOS. The paper demonstrates the hybrid multithreading realizes compactness and low overheads, which are comparable to those of TinyOS, through quantitative evaluation. The evaluation results show PAVENET OS performs 100 Hz sensor sampling with 0.01% jitter while performing wireless communication tasks, whereas optimized TinyOS has 0.62% jitter. In addition, PAVENET OS has a small footprint and low overheads (minimum RAM size: 29 bytes, minimum ROM size: 490 bytes, minimum task switch time: 23 cycles).

Writing/Thinking in Real Time: Digital Video and Corpus Query Analysis

ERIC Educational Resources Information Center

Park, Kwanghyun; Kinginger, Celeste

2010-01-01

The advance of digital video technology in the past two decades facilitates empirical investigation of learning in real time. The focus of this paper is the combined use of real-time digital video and a networked linguistic corpus for exploring the ways in which these technologies enhance our capability to investigate the cognitive process of…

Controlling Contagion Processes in Activity Driven Networks

NASA Astrophysics Data System (ADS)

Liu, Suyu; Perra, Nicola; Karsai, Márton; Vespignani, Alessandro

2014-03-01

The vast majority of strategies aimed at controlling contagion processes on networks consider the connectivity pattern of the system either quenched or annealed. However, in the real world, many networks are highly dynamical and evolve, in time, concurrently with the contagion process. Here, we derive an analytical framework for the study of control strategies specifically devised for a class of time-varying networks, namely activity-driven networks. We develop a block variable mean-field approach that allows the derivation of the equations describing the coevolution of the contagion process and the network dynamic. We derive the critical immunization threshold and assess the effectiveness of three different control strategies. Finally, we validate the theoretical picture by simulating numerically the spreading process and control strategies in both synthetic networks and a large-scale, real-world, mobile telephone call data set.

Temporal efficiency evaluation and small-worldness characterization in temporal networks

PubMed Central

Dai, Zhongxiang; Chen, Yu; Li, Junhua; Fam, Johnson; Bezerianos, Anastasios; Sun, Yu

2016-01-01

Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic systems. Here, we introduce a novel generalized analytical framework for temporal networks, which enables 1) robust evaluation of the efficiency of temporal information exchange using two new network metrics and 2) quantitative inspection of the temporal small-worldness. Specifically, we define new robust temporal network efficiency measures by incorporating the time dependency of temporal distance. We propose a temporal regular network model, and based on this plus the redefined temporal efficiency metrics and widely used temporal random network models, we introduce a quantitative approach for identifying temporal small-world architectures (featuring high temporal network efficiency both globally and locally). In addition, within this framework, we can uncover network-specific dynamic structures. Applications to brain networks, international trade networks, and social networks reveal prominent temporal small-world properties with distinct dynamic network structures. We believe that the framework can provide further insight into dynamic changes in the network topology of various real-world systems and significantly promote research on temporal networks. PMID:27682314

Temporal efficiency evaluation and small-worldness characterization in temporal networks

NASA Astrophysics Data System (ADS)

Dai, Zhongxiang; Chen, Yu; Li, Junhua; Fam, Johnson; Bezerianos, Anastasios; Sun, Yu

2016-09-01

Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic systems. Here, we introduce a novel generalized analytical framework for temporal networks, which enables 1) robust evaluation of the efficiency of temporal information exchange using two new network metrics and 2) quantitative inspection of the temporal small-worldness. Specifically, we define new robust temporal network efficiency measures by incorporating the time dependency of temporal distance. We propose a temporal regular network model, and based on this plus the redefined temporal efficiency metrics and widely used temporal random network models, we introduce a quantitative approach for identifying temporal small-world architectures (featuring high temporal network efficiency both globally and locally). In addition, within this framework, we can uncover network-specific dynamic structures. Applications to brain networks, international trade networks, and social networks reveal prominent temporal small-world properties with distinct dynamic network structures. We believe that the framework can provide further insight into dynamic changes in the network topology of various real-world systems and significantly promote research on temporal networks.

Cellular computational generalized neuron network for frequency situational intelligence in a multi-machine power system.

PubMed

Wei, Yawei; Venayagamoorthy, Ganesh Kumar

2017-09-01

To prevent large interconnected power system from a cascading failure, brownout or even blackout, grid operators require access to faster than real-time information to make appropriate just-in-time control decisions. However, the communication and computational system limitations of currently used supervisory control and data acquisition (SCADA) system can only deliver delayed information. However, the deployment of synchrophasor measurement devices makes it possible to capture and visualize, in near-real-time, grid operational data with extra granularity. In this paper, a cellular computational network (CCN) approach for frequency situational intelligence (FSI) in a power system is presented. The distributed and scalable computing unit of the CCN framework makes it particularly flexible for customization for a particular set of prediction requirements. Two soft-computing algorithms have been implemented in the CCN framework: a cellular generalized neuron network (CCGNN) and a cellular multi-layer perceptron network (CCMLPN), for purposes of providing multi-timescale frequency predictions, ranging from 16.67 ms to 2 s. These two developed CCGNN and CCMLPN systems were then implemented on two different scales of power systems, one of which installed a large photovoltaic plant. A real-time power system simulator at weather station within the Real-Time Power and Intelligent Systems (RTPIS) laboratory at Clemson, SC, was then used to derive typical FSI results. Copyright © 2017 Elsevier Ltd. All rights reserved.

The EMBRACE Magnetometer Network in South America: Network Description and Firsts Results

NASA Astrophysics Data System (ADS)

Denardini, Clezio Marcos

We present the new EMBRACE Magnetometer Network in South America, which so far is planned to cover most of the Easter Southern American longitudinal sector deploying magnetometer in several locations. We discuss the purpose and scientific goals of the network, associated with the Low- and Mid-Latitude Ionospheric Currents and Space Weather. We provide details on the instrumentation, the inter-calibration procedure, and installations of equipments already installed. In addition, we present and discuss details on the data storage, near-real time display and availability. Finally, we provide some of the first results we already achieved from this network, including the development of new real time magnetic regional indices for South America. Contacting Author: C. M. Denardini (clezio.denardin@inpe.br)

High-speed and high-fidelity system and method for collecting network traffic

DOEpatents

Weigle, Eric H [Los Alamos, NM

2010-08-24

A system is provided for the high-speed and high-fidelity collection of network traffic. The system can collect traffic at gigabit-per-second (Gbps) speeds, scale to terabit-per-second (Tbps) speeds, and support additional functions such as real-time network intrusion detection. The present system uses a dedicated operating system for traffic collection to maximize efficiency, scalability, and performance. A scalable infrastructure and apparatus for the present system is provided by splitting the work performed on one host onto multiple hosts. The present system simultaneously addresses the issues of scalability, performance, cost, and adaptability with respect to network monitoring, collection, and other network tasks. In addition to high-speed and high-fidelity network collection, the present system provides a flexible infrastructure to perform virtually any function at high speeds such as real-time network intrusion detection and wide-area network emulation for research purposes.

Concept, Implementation and Testing of PRESTo: Real-time experimentation in Southern Italy and worldwide applications

NASA Astrophysics Data System (ADS)

Zollo, Aldo; Emolo, Antonio; Festa, Gaetano; Picozzi, Matteo; Elia, Luca; Martino, Claudio; Colombelli, Simona; Brondi, Piero; Caruso, Alessandro

2016-04-01

The past two decades have witnessed a huge progress in the development, implementation and testing of Earthquakes Early Warning Systems (EEWS) worldwide, as the result of a joint effort of the seismological and earthquake engineering communities to set up robust and efficient methodologies for the real-time seismic risk mitigation. This work presents an overview of the worldwide applications of the system PRESTo (PRobabilistic and Evolutionary early warning SysTem), which is the highly configurable and easily portable platform for Earthquake Early Warning developed by the RISSCLab group of the University of Naples Federico II. In particular, we first present the results of the real-time experimentation of PRESTo in Suthern Italy on the data streams of the Irpinia Seismic Network (ISNet), in Southern Italy. ISNet is a dense high-dynamic range, earthquake observing system, which operates in true real-time mode, thanks to a mixed data transmission system based on proprietary digital terrestrial links, standard ADSL and UMTS technologies. Using the seedlink protocol data are transferred to the network center unit, running the software platform PRESTo which is devoted to process the real-time data streaming, estimate source parameters and issue the alert. The software platform PRESTo uses a P-wave, network-based approach which has evolved and improved during the time since its first release. In its original version consisted in a series of modules, aimed at the event detection/picking, probabilistic real-time earthquake location and magnitude estimation, prediction of peak ground motion at distant sites through ground motion prediction equations for the area. In the recent years, PRESTo has been also implemented at the accelerometric and broad-band seismic networks in South Korea, Romania, North-East Italy, and Turkey and off-line tested in Iberian Peninsula, Israel, and Japan. Moreover, the feasibility of a PRESTo-based, EEWS at national scale in Italy, has been tested by evaluating its performance for the Italian Accelerometric Network. These testing experiments and the EEWS performance results will be summarized in the near-future perspective of building the next generation of early warning systems.

Road Risk Modeling and Cloud-Aided Safety-Based Route Planning.

PubMed

Li, Zhaojian; Kolmanovsky, Ilya; Atkins, Ella; Lu, Jianbo; Filev, Dimitar P; Michelini, John

2016-11-01

This paper presents a safety-based route planner that exploits vehicle-to-cloud-to-vehicle (V2C2V) connectivity. Time and road risk index (RRI) are considered as metrics to be balanced based on user preference. To evaluate road segment risk, a road and accident database from the highway safety information system is mined with a hybrid neural network model to predict RRI. Real-time factors such as time of day, day of the week, and weather are included as correction factors to the static RRI prediction. With real-time RRI and expected travel time, route planning is formulated as a multiobjective network flow problem and further reduced to a mixed-integer programming problem. A V2C2V implementation of our safety-based route planning approach is proposed to facilitate access to real-time information and computing resources. A real-world case study, route planning through the city of Columbus, Ohio, is presented. Several scenarios illustrate how the "best" route can be adjusted to favor time versus safety metrics.

Random graph models for dynamic networks

NASA Astrophysics Data System (ADS)

Zhang, Xiao; Moore, Cristopher; Newman, Mark E. J.

2017-10-01

Recent theoretical work on the modeling of network structure has focused primarily on networks that are static and unchanging, but many real-world networks change their structure over time. There exist natural generalizations to the dynamic case of many static network models, including the classic random graph, the configuration model, and the stochastic block model, where one assumes that the appearance and disappearance of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. Here we give an introduction to this class of models, showing for instance how one can compute their equilibrium properties. We also demonstrate their use in data analysis and statistical inference, giving efficient algorithms for fitting them to observed network data using the method of maximum likelihood. This allows us, for example, to estimate the time constants of network evolution or infer community structure from temporal network data using cues embedded both in the probabilities over time that node pairs are connected by edges and in the characteristic dynamics of edge appearance and disappearance. We illustrate these methods with a selection of applications, both to computer-generated test networks and real-world examples.

Recurrent Neural Network for Computing the Drazin Inverse.

PubMed

Stanimirović, Predrag S; Zivković, Ivan S; Wei, Yimin

2015-11-01

This paper presents a recurrent neural network (RNN) for computing the Drazin inverse of a real matrix in real time. This recurrent neural network (RNN) is composed of n independent parts (subnetworks), where n is the order of the input matrix. These subnetworks can operate concurrently, so parallel and distributed processing can be achieved. In this way, the computational advantages over the existing sequential algorithms can be attained in real-time applications. The RNN defined in this paper is convenient for an implementation in an electronic circuit. The number of neurons in the neural network is the same as the number of elements in the output matrix, which represents the Drazin inverse. The difference between the proposed RNN and the existing ones for the Drazin inverse computation lies in their network architecture and dynamics. The conditions that ensure the stability of the defined RNN as well as its convergence toward the Drazin inverse are considered. In addition, illustrative examples and examples of application to the practical engineering problems are discussed to show the efficacy of the proposed neural network.

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

Wilson, David G.; Cook, Marvin A.

This report summarizes collaborative efforts between Secure Scalable Microgrid and Korean Institute of Energy Research team members . The efforts aim to advance microgrid research and development towards the efficient utilization of networked microgrids . The collaboration resulted in the identification of experimental and real time simulation capabilities that may be leveraged for networked microgrids research, development, and demonstration . Additional research was performed to support the demonstration of control techniques within real time simulation and with hardware in the loop for DC microgrids .

Development of a network RTK positioning and gravity-surveying application with gravity correction using a smartphone.

PubMed

Kim, Jinsoo; Lee, Youngcheol; Cha, Sungyeoul; Choi, Chuluong; Lee, Seongkyu

2013-07-12

This paper proposes a smartphone-based network real-time kinematic (RTK) positioning and gravity-surveying application (app) that allows semi-real-time measurements using the built-in Bluetooth features of the smartphone and a third-generation or long-term evolution wireless device. The app was implemented on a single smartphone by integrating a global navigation satellite system (GNSS) controller, a laptop, and a field-note writing tool. The observation devices (i.e., a GNSS receiver and relative gravimeter) functioned independently of this system. The app included a gravity module, which converted the measured relative gravity reading into an absolute gravity value according to tides; meter height; instrument drift correction; and network adjustments. The semi-real-time features of this app allowed data to be shared easily with other researchers. Moreover, the proposed smartphone-based gravity-survey app was easily adaptable to various locations and rough terrain due to its compact size.

Real-time EEG-based detection of fatigue driving danger for accident prediction.

PubMed

Wang, Hong; Zhang, Chi; Shi, Tianwei; Wang, Fuwang; Ma, Shujun

2015-03-01

This paper proposes a real-time electroencephalogram (EEG)-based detection method of the potential danger during fatigue driving. To determine driver fatigue in real time, wavelet entropy with a sliding window and pulse coupled neural network (PCNN) were used to process the EEG signals in the visual area (the main information input route). To detect the fatigue danger, the neural mechanism of driver fatigue was analyzed. The functional brain networks were employed to track the fatigue impact on processing capacity of brain. The results show the overall functional connectivity of the subjects is weakened after long time driving tasks. The regularity is summarized as the fatigue convergence phenomenon. Based on the fatigue convergence phenomenon, we combined both the input and global synchronizations of brain together to calculate the residual amount of the information processing capacity of brain to obtain the dangerous points in real time. Finally, the danger detection system of the driver fatigue based on the neural mechanism was validated using accident EEG. The time distributions of the output danger points of the system have a good agreement with those of the real accident points.

Development of a neural network technique for KSTAR Thomson scattering diagnostics.

PubMed

Lee, Seung Hun; Lee, J H; Yamada, I; Park, Jae Sun

2016-11-01

Neural networks provide powerful approaches of dealing with nonlinear data and have been successfully applied to fusion plasma diagnostics and control systems. Controlling tokamak plasmas in real time is essential to measure the plasma parameters in situ. However, the χ 2 method traditionally used in Thomson scattering diagnostics hampers real-time measurement due to the complexity of the calculations involved. In this study, we applied a neural network approach to Thomson scattering diagnostics in order to calculate the electron temperature, comparing the results to those obtained with the χ 2 method. The best results were obtained for 10 3 training cycles and eight nodes in the hidden layer. Our neural network approach shows good agreement with the χ 2 method and performs the calculation twenty times faster.

Real-time neural network earthquake profile predictor

DOEpatents

Leach, R.R.; Dowla, F.U.

1996-02-06

A neural network has been developed that uses first-arrival energy to predict the characteristics of impending earthquake seismograph signals. The propagation of ground motion energy through the earth is a highly nonlinear function. This is due to different forms of ground motion as well as to changes in the elastic properties of the media throughout the propagation path. The neural network is trained using seismogram data from earthquakes. Presented with a previously unseen earthquake, the neural network produces a profile of the complete earthquake signal using data from the first seconds of the signal. This offers a significant advance in the real-time monitoring, warning, and subsequent hazard minimization of catastrophic ground motion. 17 figs.

Real-time neural network earthquake profile predictor

DOEpatents

Leach, Richard R.; Dowla, Farid U.

1996-01-01

A neural network has been developed that uses first-arrival energy to predict the characteristics of impending earthquake seismograph signals. The propagation of ground motion energy through the earth is a highly nonlinear function. This is due to different forms of ground motion as well as to changes in the elastic properties of the media throughout the propagation path. The neural network is trained using seismogram data from earthquakes. Presented with a previously unseen earthquake, the neural network produces a profile of the complete earthquake signal using data from the first seconds of the signal. This offers a significant advance in the real-time monitoring, warning, and subsequent hazard minimization of catastrophic ground motion.

PILOT: An intelligent distributed operations support system

NASA Technical Reports Server (NTRS)

Rasmussen, Arthur N.

1993-01-01

The Real-Time Data System (RTDS) project is exploring the application of advanced technologies to the real-time flight operations environment of the Mission Control Centers at NASA's Johnson Space Center. The system, based on a network of engineering workstations, provides services such as delivery of real time telemetry data to flight control applications. To automate the operation of this complex distributed environment, a facility called PILOT (Process Integrity Level and Operation Tracker) is being developed. PILOT comprises a set of distributed agents cooperating with a rule-based expert system; together they monitor process operation and data flows throughout the RTDS network. The goal of PILOT is to provide unattended management and automated operation under user control.

Viability of using energy storage for frequency regulation on power grid

NASA Astrophysics Data System (ADS)

Lim, Y. S.; Hau, L. C.; Loh, K. Y.; Lim, K. Y.; Lyons, P. F.; Taylor, P. C.

2018-05-01

This project is about the development and integration of a real-time network simulator in the laboratory using hardware in the loop (HIL) for the purpose of frequency regulation. Frequency regulation is done using the energy storage system (ESS) and a real-time network test bed developed in the smart energy laboratory in Newcastle University. An IEEE Test System was built in the OPAL-RT network simulator to mimic the power grid with renewable energy sources. The study demonstrates the viability of using an ESS to regulate the frequency under an increased penetration of renewable energy sources.

Efficient evaluation of wireless real-time control networks.

PubMed

Horvath, Peter; Yampolskiy, Mark; Koutsoukos, Xenofon

2015-02-11

In this paper, we present a system simulation framework for the design and performance evaluation of complex wireless cyber-physical systems. We describe the simulator architecture and the specific developments that are required to simulate cyber-physical systems relying on multi-channel, multihop mesh networks. We introduce realistic and efficient physical layer models and a system simulation methodology, which provides statistically significant performance evaluation results with low computational complexity. The capabilities of the proposed framework are illustrated in the example of WirelessHART, a centralized, real-time, multi-hop mesh network designed for industrial control and monitor applications.

Prototype real-time baseband signal combiner. [deep space network

NASA Technical Reports Server (NTRS)

Howard, L. D.

1980-01-01

The design and performance of a prototype real-time baseband signal combiner, used to enhance the received Voyager 2 spacecraft signals during the Jupiter flyby, is described. Hardware delay paths, operating programs, and firmware are discussed.

A Pre-Emptive Horizontal Channel Borrowing and Vertical Traffic Overflowing Channel Allocation Scheme for Overlay Networks

NASA Astrophysics Data System (ADS)

Zhao, Fang-Ming; Jiang, Ling-Ge; He, Chen

In this paper, a channel allocation scheme is studied for overlay wireless networks to optimize connection-level QoS. The contributions of our work are threefold. First, a channel allocation strategy using both horizontal channel borrowing and vertical traffic overflowing (HCBVTO) is presented and analyzed. When all the channels in a given macrocell are used, high-mobility real-time handoff requests can borrow channels from adjacent homogeneous cells. In case that the borrowing requests fail, handoff requests may also be overflowed to heterogeneous cells, if possible. Second, high-mobility real-time service is prioritized by allowing it to preempt channels currently used by other services. And third, to meet the high QoS requirements of some services and increase the utilization of radio resources, certain services can be transformed between real-time services and non-real-time services as necessary. Simulation results demonstrate that the proposed schemes can improve system performance.

Real-Time Mapping alert system; characteristics and capabilities

USGS Publications Warehouse

Torres, L.A.; Lambert, S.C.; Liebermann, T.D.

1995-01-01

The U.S. Geological Survey has an extensive hydrologic network that records and transmits precipitation, stage, discharge, and other water-related data on a real-time basis to an automated data processing system. Data values are recorded on electronic data collection platforms at field sampling sites. These values are transmitted by means of orbiting satellites to receiving ground stations, and by way of telecommunication lines to a U.S. Geological Survey office where they are processed on a computer system. Data that exceed predefined thresholds are identified as alert values. The current alert status at monitoring sites within a state or region is of critical importance during floods, hurricanes, and other extreme hydrologic events. This report describes the characteristics and capabilities of a series of computer programs for real-time mapping of hydrologic data. The software provides interactive graphics display and query of hydrologic information from the network in a real-time, map-based, menu-driven environment.

Extensions to the Parallel Real-Time Artificial Intelligence System (PRAIS) for fault-tolerant heterogeneous cycle-stealing reasoning

NASA Technical Reports Server (NTRS)

Goldstein, David

1991-01-01

Extensions to an architecture for real-time, distributed (parallel) knowledge-based systems called the Parallel Real-time Artificial Intelligence System (PRAIS) are discussed. PRAIS strives for transparently parallelizing production (rule-based) systems, even under real-time constraints. PRAIS accomplished these goals (presented at the first annual C Language Integrated Production System (CLIPS) conference) by incorporating a dynamic task scheduler, operating system extensions for fact handling, and message-passing among multiple copies of CLIPS executing on a virtual blackboard. This distributed knowledge-based system tool uses the portability of CLIPS and common message-passing protocols to operate over a heterogeneous network of processors. Results using the original PRAIS architecture over a network of Sun 3's, Sun 4's and VAX's are presented. Mechanisms using the producer-consumer model to extend the architecture for fault-tolerance and distributed truth maintenance initiation are also discussed.

Real Time Distributed Embedded Oscillator Operating Frequency Monitoring

NASA Technical Reports Server (NTRS)

Pollock, Julie (Inventor); Oliver, Brett D. (Inventor); Brickner, Christopher (Inventor)

2013-01-01

A method for clock monitoring in a network is provided. The method comprises receiving a first network clock signal at a network device and comparing the first network clock signal to a local clock signal from a primary oscillator coupled to the network device.

Real-World Neuroimaging Technologies

DTIC Science & Technology

2013-05-10

system enables long-term wear of up to 10 consecutive hours of operation time. The system’s wireless technologies, light weight (200g), and dry sensor ...biomarkers, body sensor networks , brain computer interactionbrain, computer interfaces, data acquisition, electroencephalography monitoring, translational...brain activity in real-world scenarios. INDEX TERMS Behavioral science, biomarkers, body sensor networks , brain computer interfaces, brain computer

Real time estimation of arterial travel time and operational measures through integration of real time fixed sensor data and simulation.

DOT National Transportation Integrated Search

2012-02-01

A wide variety of advanced technological tools have been implemented throughout Georgias : transportation network to increase its efficiency. These systems are credited with reducing or : maintaining freeway congestion levels in light of increasin...

Fault detection and diagnosis using neural network approaches

NASA Technical Reports Server (NTRS)

Kramer, Mark A.

1992-01-01

Neural networks can be used to detect and identify abnormalities in real-time process data. Two basic approaches can be used, the first based on training networks using data representing both normal and abnormal modes of process behavior, and the second based on statistical characterization of the normal mode only. Given data representative of process faults, radial basis function networks can effectively identify failures. This approach is often limited by the lack of fault data, but can be facilitated by process simulation. The second approach employs elliptical and radial basis function neural networks and other models to learn the statistical distributions of process observables under normal conditions. Analytical models of failure modes can then be applied in combination with the neural network models to identify faults. Special methods can be applied to compensate for sensor failures, to produce real-time estimation of missing or failed sensors based on the correlations codified in the neural network.

Real-time object-to-features vectorisation via Siamese neural networks

NASA Astrophysics Data System (ADS)

Fedorenko, Fedor; Usilin, Sergey

2017-03-01

Object-to-features vectorisation is a hard problem to solve for objects that can be hard to distinguish. Siamese and Triplet neural networks are one of the more recent tools used for such task. However, most networks used are very deep networks that prove to be hard to compute in the Internet of Things setting. In this paper, a computationally efficient neural network is proposed for real-time object-to-features vectorisation into a Euclidean metric space. We use L2 distance to reflect feature vector similarity during both training and testing. In this way, feature vectors we develop can be easily classified using K-Nearest Neighbours classifier. Such approach can be used to train networks to vectorise such "problematic" objects like images of human faces, keypoint image patches, like keypoints on Arctic maps and surrounding marine areas.

Real-time hydraulic interval state estimation for water transport networks: a case study

NASA Astrophysics Data System (ADS)

Vrachimis, Stelios G.; Eliades, Demetrios G.; Polycarpou, Marios M.

2018-03-01

Hydraulic state estimation in water distribution networks is the task of estimating water flows and pressures in the pipes and nodes of the network based on some sensor measurements. This requires a model of the network as well as knowledge of demand outflow and tank water levels. Due to modeling and measurement uncertainty, standard state estimation may result in inaccurate hydraulic estimates without any measure of the estimation error. This paper describes a methodology for generating hydraulic state bounding estimates based on interval bounds on the parametric and measurement uncertainties. The estimation error bounds provided by this method can be applied to determine the existence of unaccounted-for water in water distribution networks. As a case study, the method is applied to a modified transport network in Cyprus, using actual data in real time.

Optical chaos and hybrid WDM/TDM based large capacity quasi-distributed sensing network with real-time fiber fault monitoring.

PubMed

Luo, Yiyang; Xia, Li; Xu, Zhilin; Yu, Can; Sun, Qizhen; Li, Wei; Huang, Di; Liu, Deming

2015-02-09

An optical chaos and hybrid wavelength division multiplexing/time division multiplexing (WDM/TDM) based large capacity quasi-distributed sensing network with real-time fiber fault monitoring is proposed. Chirped fiber Bragg grating (CFBG) intensity demodulation is adopted to improve the dynamic range of the measurements. Compared with the traditional sensing interrogation methods in time, radio frequency and optical wavelength domains, the measurand sensing and the precise locating of the proposed sensing network can be simultaneously interrogated by the relative amplitude change (RAC) and the time delay of the correlation peak in the cross-correlation spectrum. Assisted with the WDM/TDM technology, hundreds of sensing units could be potentially multiplexed in the multiple sensing fiber lines. Based on the proof-of-concept experiment for axial strain measurement with three sensing fiber lines, the strain sensitivity up to 0.14% RAC/με and the precise locating of the sensors are achieved. Significantly, real-time fiber fault monitoring in the three sensing fiber lines is also implemented with a spatial resolution of 2.8 cm.

An energy-efficient transmission scheme for real-time data in wireless sensor networks.

PubMed

Kim, Jin-Woo; Barrado, José Ramón Ramos; Jeon, Dong-Keun

2015-05-20

The Internet of things (IoT) is a novel paradigm where all things or objects in daily life can communicate with other devices and provide services over the Internet. Things or objects need identifying, sensing, networking and processing capabilities to make the IoT paradigm a reality. The IEEE 802.15.4 standard is one of the main communication protocols proposed for the IoT. The IEEE 802.15.4 standard provides the guaranteed time slot (GTS) mechanism that supports the quality of service (QoS) for the real-time data transmission. In spite of some QoS features in IEEE 802.15.4 standard, the problem of end-to-end delay still remains. In order to solve this problem, we propose a cooperative medium access scheme (MAC) protocol for real-time data transmission. We also evaluate the performance of the proposed scheme through simulation. The simulation results demonstrate that the proposed scheme can improve the network performance.

An Energy-Efficient Transmission Scheme for Real-Time Data in Wireless Sensor Networks

PubMed Central

Kim, Jin-Woo; Barrado, José Ramón Ramos; Jeon, Dong-Keun

2015-01-01

The Internet of things (IoT) is a novel paradigm where all things or objects in daily life can communicate with other devices and provide services over the Internet. Things or objects need identifying, sensing, networking and processing capabilities to make the IoT paradigm a reality. The IEEE 802.15.4 standard is one of the main communication protocols proposed for the IoT. The IEEE 802.15.4 standard provides the guaranteed time slot (GTS) mechanism that supports the quality of service (QoS) for the real-time data transmission. In spite of some QoS features in IEEE 802.15.4 standard, the problem of end-to-end delay still remains. In order to solve this problem, we propose a cooperative medium access scheme (MAC) protocol for real-time data transmission. We also evaluate the performance of the proposed scheme through simulation. The simulation results demonstrate that the proposed scheme can improve the network performance. PMID:26007722

Real-time scalable visual analysis on mobile devices

NASA Astrophysics Data System (ADS)

Pattath, Avin; Ebert, David S.; May, Richard A.; Collins, Timothy F.; Pike, William

2008-02-01

Interactive visual presentation of information can help an analyst gain faster and better insight from data. When combined with situational or context information, visualization on mobile devices is invaluable to in-field responders and investigators. However, several challenges are posed by the form-factor of mobile devices in developing such systems. In this paper, we classify these challenges into two broad categories - issues in general mobile computing and issues specific to visual analysis on mobile devices. Using NetworkVis and Infostar as example systems, we illustrate some of the techniques that we employed to overcome many of the identified challenges. NetworkVis is an OpenVG-based real-time network monitoring and visualization system developed for Windows Mobile devices. Infostar is a flash-based interactive, real-time visualization application intended to provide attendees access to conference information. Linked time-synchronous visualization, stylus/button-based interactivity, vector graphics, overview-context techniques, details-on-demand and statistical information display are some of the highlights of these applications.

Real-time synchronization of wireless sensor network by 1-PPS signal

NASA Astrophysics Data System (ADS)

Giammarini, Marco; Pieralisi, Marco; Isidori, Daniela; Concettoni, Enrico; Cristalli, Cristina; Fioravanti, Matteo

2015-05-01

The use of wireless sensor networks with different nodes is desirable in a smart environment, because the network setting up and installation on preexisting structures can be done without a fixed cabled infrastructure. The flexibility of the monitoring system is fundamental where the use of a considerable quantity of cables could compromise the normal exercise, could affect the quality of acquired signal and finally increase the cost of the materials and installation. The network is composed of several intelligent "nodes", which acquires data from different kind of sensors, and then store or transmit them to a central elaboration unit. The synchronization of data acquisition is the core of the real-time wireless sensor network (WSN). In this paper, we present a comparison between different methods proposed by literature for the real-time acquisition in a WSN and finally we present our solution based on 1-Pulse-Per-Second (1-PPS) signal generated by GPS systems. The sensor node developed is a small-embedded system based on ARM microcontroller that manages the acquisition, the timing and the post-processing of the data. The communications between the sensors and the master based on IEEE 802.15.4 protocol and managed by dedicated software. Finally, we present the preliminary results obtained on a 3 floor building simulator with the wireless sensors system developed.

Real-time network security situation visualization and threat assessment based on semi-Markov process

NASA Astrophysics Data System (ADS)

Chen, Junhua

2013-03-01

To cope with a large amount of data in current sensed environments, decision aid tools should provide their understanding of situations in a time-efficient manner, so there is an increasing need for real-time network security situation awareness and threat assessment. In this study, the state transition model of vulnerability in the network based on semi-Markov process is proposed at first. Once events are triggered by an attacker's action or system response, the current states of the vulnerabilities are known. Then we calculate the transition probabilities of the vulnerability from the current state to security failure state. Furthermore in order to improve accuracy of our algorithms, we adjust the probabilities that they exploit the vulnerability according to the attacker's skill level. In the light of the preconditions and post-conditions of vulnerabilities in the network, attack graph is built to visualize security situation in real time. Subsequently, we predict attack path, recognize attack intention and estimate the impact through analysis of attack graph. These help administrators to insight into intrusion steps, determine security state and assess threat. Finally testing in a network shows that this method is reasonable and feasible, and can undertake tremendous analysis task to facilitate administrators' work.

The Promise and Challenges of High Rate GNSS for Environmental Monitoring and Response

NASA Astrophysics Data System (ADS)

LaBrecque, John

2017-04-01

The decadal vision Global Geodetic Observing System recognizes the potential of high rate real time GNSS for environmental monitoring. The GGOS initiated a program to advance GNSS real time high rate measurements to augment seismic and other sensor systems for earthquake and tsunami early warning. High rate multi-GNSS networks can provide ionospheric tomography for the detection and tracking of land, ocean and atmospheric gravity waves that can provide coastal warning of tsunamis induced by earthquakes, volcanic eruptions, severe weather and other catastrophic events. NASA has collaborated on a microsatellite constellation of GPS receivers to measure ocean surface roughness to improve severe storm tracking and a equatorial system of GPS occultation receivers to measure ionospheric and atmospheric dynamics. Systems such as these will be significantly enhanced by the availability of a four fold increase in GNSS satellite systems with new and enhanced signal structures and by the densification of regional multi-GNSS networks. These new GNSS capabilities will rely upon improved and cost effective communications infrastructure for a network of coordinated real time analysis centers with input to national warning systems. Most important, the implementation of these new real time GNSS capabilities will rely upon the broad international support for the sharing of real time GNSS much as is done in weather and seismic observing systems and as supported by the Committee of Experts on UN Global Geodetic Information Management (UNGGIM).

Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks

PubMed Central

2017-01-01

In the vehicular networks, the real-time video reporting service is used to send the recorded videos in the vehicle to the cloud. However, when facilitating the real-time video reporting service in the vehicular networks, the usage of the fourth generation (4G) long term evolution (LTE) was proved to suffer from latency while the IEEE 802.11p standard does not offer sufficient scalability for a such congested environment. To overcome those drawbacks, the fifth-generation (5G)-enabled vehicular network is considered as a promising technology for empowering the real-time video reporting service. In this paper, we note that security and privacy related issues should also be carefully addressed to boost the early adoption of 5G-enabled vehicular networks. There exist a few research works for secure video reporting service in 5G-enabled vehicular networks. However, their usage is limited because of public key certificates and expensive pairing operations. Thus, we propose a secure and lightweight protocol for cloud-assisted video reporting service in 5G-enabled vehicular networks. Compared to the conventional public key certificates, the proposed protocol achieves entities’ authorization through anonymous credential. Also, by using lightweight security primitives instead of expensive bilinear pairing operations, the proposed protocol minimizes the computational overhead. From the evaluation results, we show that the proposed protocol takes the smaller computation and communication time for the cryptographic primitives than that of the well-known Eiza-Ni-Shi protocol. PMID:28946633

Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks.

PubMed

Nkenyereye, Lewis; Kwon, Joonho; Choi, Yoon-Ho

2017-09-23

In the vehicular networks, the real-time video reporting service is used to send the recorded videos in the vehicle to the cloud. However, when facilitating the real-time video reporting service in the vehicular networks, the usage of the fourth generation (4G) long term evolution (LTE) was proved to suffer from latency while the IEEE 802.11p standard does not offer sufficient scalability for a such congested environment. To overcome those drawbacks, the fifth-generation (5G)-enabled vehicular network is considered as a promising technology for empowering the real-time video reporting service. In this paper, we note that security and privacy related issues should also be carefully addressed to boost the early adoption of 5G-enabled vehicular networks. There exist a few research works for secure video reporting service in 5G-enabled vehicular networks. However, their usage is limited because of public key certificates and expensive pairing operations. Thus, we propose a secure and lightweight protocol for cloud-assisted video reporting service in 5G-enabled vehicular networks. Compared to the conventional public key certificates, the proposed protocol achieves entities' authorization through anonymous credential. Also, by using lightweight security primitives instead of expensive bilinear pairing operations, the proposed protocol minimizes the computational overhead. From the evaluation results, we show that the proposed protocol takes the smaller computation and communication time for the cryptographic primitives than that of the well-known Eiza-Ni-Shi protocol.

Resonator memories and optical novelty filters

NASA Astrophysics Data System (ADS)

Anderson, Dana Z.; Erle, Marie C.

Optical resonators having holographic elements are potential candidates for storing information that can be accessed through content addressable or associative recall. Closely related to the resonator memory is the optical novelty filter, which can detect the differences between a test object and a set of reference objects. We discuss implementations of these devices using continuous optical media such as photorefractive materials. The discussion is framed in the context of neural network models. There are both formal and qualitative similarities between the resonator memory and optical novelty filter and network models. Mode competition arises in the theory of the resonator memory, much as it does in some network models. We show that the role of the phenomena of "daydreaming" in the real-time programmable optical resonator is very much akin to the role of "unlearning" in neural network memories. The theory of programming the real-time memory for a single mode is given in detail. This leads to a discussion of the optical novelty filter. Experimental results for the resonator memory, the real-time programmable memory, and the optical tracking novelty filter are reviewed. We also point to several issues that need to be addressed in order to implement more formal models of neural networks.

Resonator Memories And Optical Novelty Filters

NASA Astrophysics Data System (ADS)

Anderson, Dana Z.; Erie, Marie C.

1987-05-01

Optical resonators having holographic elements are potential candidates for storing information that can be accessed through content-addressable or associative recall. Closely related to the resonator memory is the optical novelty filter, which can detect the differences between a test object and a set of reference objects. We discuss implementations of these devices using continuous optical media such as photorefractive ma-terials. The discussion is framed in the context of neural network models. There are both formal and qualitative similarities between the resonator memory and optical novelty filter and network models. Mode competition arises in the theory of the resonator memory, much as it does in some network models. We show that the role of the phenomena of "daydream-ing" in the real-time programmable optical resonator is very much akin to the role of "unlearning" in neural network memories. The theory of programming the real-time memory for a single mode is given in detail. This leads to a discussion of the optical novelty filter. Experimental results for the resonator memory, the real-time programmable memory, and the optical tracking novelty filter are reviewed. We also point to several issues that need to be addressed in order to implement more formal models of neural networks.

Real-time sensor data validation

NASA Technical Reports Server (NTRS)

Bickmore, Timothy W.

1994-01-01

This report describes the status of an on-going effort to develop software capable of detecting sensor failures on rocket engines in real time. This software could be used in a rocket engine controller to prevent the erroneous shutdown of an engine due to sensor failures which would otherwise be interpreted as engine failures by the control software. The approach taken combines analytical redundancy with Bayesian belief networks to provide a solution which has well defined real-time characteristics and well-defined error rates. Analytical redundancy is a technique in which a sensor's value is predicted by using values from other sensors and known or empirically derived mathematical relations. A set of sensors and a set of relations among them form a network of cross-checks which can be used to periodically validate all of the sensors in the network. Bayesian belief networks provide a method of determining if each of the sensors in the network is valid, given the results of the cross-checks. This approach has been successfully demonstrated on the Technology Test Bed Engine at the NASA Marshall Space Flight Center. Current efforts are focused on extending the system to provide a validation capability for 100 sensors on the Space Shuttle Main Engine.

Comparative Analysis of Neural Network Training Methods in Real-time Radiotherapy.

PubMed

Nouri, S; Hosseini Pooya, S M; Soltani Nabipour, J

2017-03-01

The motions of body and tumor in some regions such as chest during radiotherapy treatments are one of the major concerns protecting normal tissues against high doses. By using real-time radiotherapy technique, it is possible to increase the accuracy of delivered dose to the tumor region by means of tracing markers on the body of patients. This study evaluates the accuracy of some artificial intelligence methods including neural network and those of combination with genetic algorithm as well as particle swarm optimization (PSO) estimating tumor positions in real-time radiotherapy. One hundred recorded signals of three external markers were used as input data. The signals from 3 markers thorough 10 breathing cycles of a patient treated via a cyber-knife for a lung tumor were used as data input. Then, neural network method and its combination with genetic or PSO algorithms were applied determining the tumor locations using MATLAB© software program. The accuracies were obtained 0.8%, 12% and 14% in neural network, genetic and particle swarm optimization algorithms, respectively. The internal target volume (ITV) should be determined based on the applied neural network algorithm on training steps.

GNSS Active Network of West of Sao Paulo State Applied to Ionosphere Monitoring

NASA Astrophysics Data System (ADS)

Aguiar, C. R.; Camargo, P. D.

2008-12-01

In Brazil, a research project of atmospheric studies from reference stations equipped with dual frequency GNSS receivers is in initial phase. These stations have composed the GNSS Active Network of West Sao Paulo State (Network-GNSS-SP) and have been broadcasting GNSS data in real time. Network-GNSS-SP is in tests phase and it's the first Brazilian network to provide GNSS measurements in real time. In Spatial Geodesy Study Brazilian Group (GEGE) has been researched the ionosphere effects on L band signal, as well as the GPS potential on ionosphere dynamic monitoring and, consequently, the application of this one to spatial geophysics study, besides dynamic ionosphere modeling. An algorithm based on Kalman filter has been developed for ionosphere modeling at low latitude regions and estimation of ionospheric parameters as absolute vertical TEC (VTEC) for the monitoring of ionosphere behavior. The approach used in this study is to apply a model for the ionospheric vertical delay. In the algorithm, the ionospheric vertical delay is modeled and expanded by Fourier series. In this paper has been realized on-line processing of the Network-GNSS-SP data and the initial results reached with the algorithm can already be analyzed. The results show the ionospheric maps created from real time TEC estimates.

A Tree Based Broadcast Scheme for (m, k)-firm Real-Time Stream in Wireless Sensor Networks

PubMed Central

Park, HoSung; Kim, Beom-Su; Kim, Kyong Hoon; Shah, Babar; Kim, Ki-Il

2017-01-01

Recently, various unicast routing protocols have been proposed to deliver measured data from the sensor node to the sink node within the predetermined deadline in wireless sensor networks. In parallel with their approaches, some applications demand the specific service, which is based on broadcast to all nodes within the deadline, the feasible real-time traffic model and improvements in energy efficiency. However, current protocols based on either flooding or one-to-one unicast cannot meet the above requirements entirely. Moreover, as far as the authors know, there is no study for the real-time broadcast protocol to support the application-specific traffic model in WSN yet. Based on the above analysis, in this paper, we propose a new (m, k)-firm-based Real-time Broadcast Protocol (FRBP) by constructing a broadcast tree to satisfy the (m, k)-firm, which is applicable to the real-time model in resource-constrained WSNs. The broadcast tree in FRBP is constructed by the distance-based priority scheme, whereas energy efficiency is improved by selecting as few as nodes on a tree possible. To overcome the unstable network environment, the recovery scheme invokes rapid partial tree reconstruction in order to designate another node as the parent on a tree according to the measured (m, k)-firm real-time condition and local states monitoring. Finally, simulation results are given to demonstrate the superiority of FRBP compared to the existing schemes in terms of average deadline missing ratio, average throughput and energy consumption. PMID:29120404

Spatio-temporal networks: reachability, centrality and robustness.

PubMed

Williams, Matthew J; Musolesi, Mirco

2016-06-01

Recent advances in spatial and temporal networks have enabled researchers to more-accurately describe many real-world systems such as urban transport networks. In this paper, we study the response of real-world spatio-temporal networks to random error and systematic attack, taking a unified view of their spatial and temporal performance. We propose a model of spatio-temporal paths in time-varying spatially embedded networks which captures the property that, as in many real-world systems, interaction between nodes is non-instantaneous and governed by the space in which they are embedded. Through numerical experiments on three real-world urban transport systems, we study the effect of node failure on a network's topological, temporal and spatial structure. We also demonstrate the broader applicability of this framework to three other classes of network. To identify weaknesses specific to the behaviour of a spatio-temporal system, we introduce centrality measures that evaluate the importance of a node as a structural bridge and its role in supporting spatio-temporally efficient flows through the network. This exposes the complex nature of fragility in a spatio-temporal system, showing that there is a variety of failure modes when a network is subject to systematic attacks.

The IRIS DMC: Perspectives on Real-Time Data Management and Open Access From a Large Seismological Archive: Challenges, Tools, and Quality Assurance

NASA Astrophysics Data System (ADS)

Benson, R. B.

2007-05-01

The IRIS Data Management Center, located in Seattle, WA, is the largest openly accessible geophysical archive in the world, and has a unique perspective on data management and operational practices that gets the most out of your network. Networks scale broad domains in time and space, from finite needs to monitor bridges and dams to national and international networks like the GSN and the FDSN that establish a baseline for global monitoring and research, the requirements that go into creating a well-tuned DMC archive treat these the same, building a collaborative network of networks that generations of users rely on and adds value to the data. Funded by the National Science Foundation through the Division of Earth Sciences, IRIS is operated through member universities and in cooperation with the USGS, and the DMS facility is a bridge between a globally distributed collaboration of seismic networks and an equally distributed network of users that demand a high standard for data quality, completeness, and ease of access. I will describe the role that a perpetual archive has in the life cycle of data, and how hosting real-time data performs a dual role of being a hub for continuous data from approximately 59 real-time networks, and distributing these (along with other data from the 40-year library of available time-series data) to researchers, while simultaneously providing shared data back to networks in real- time that benefits monitoring activities. I will describe aspects of our quality-assurance framework that are both passively and actively performed on 1100 seismic stations, generating over 6,000 channels of regularly sampled data arriving daily, that data providers can use as aids in operating their network, and users can likewise use when requesting suitable data for research purposes. The goal of the DMC is to eliminate bottlenecks in data discovery and shortening the steps leading to analysis. This includes many challenges, including keeping metadata current, tools for evaluating and viewing them, along with measuring and creating databases of other performance metrics and how monitoring them closer to real- time helps reduce operation costs, creates a richer repository, and eliminates problems over generations of duty cycles of data usage. I will describe a new resource, called the Nominal Response Library, which hopes to provide accurate and representative examples of sensor and data logger configurations that are hosted at the DMC and constitute a high-graded subset for crafting your own metadata. Finally, I want to encourage all network operators who do not currently submit SEED format data to an archive to consider these benefits, and briefly discuss how robust transfer mechanisms that include Earthworm, LISS, Antelope, NRTS and SeisComp, to name a few, can assist you in contributing your network data and help create this enabling virtual network of networks. In this era of high performance Internet capacity, the process that enables others to share your data and allows you to utilize external sources of data is nearly seamless with your current mission of network operation.

A BHR Composite Network-Based Visualization Method for Deformation Risk Level of Underground Space

PubMed Central

Zheng, Wei; Zhang, Xiaoya; Lu, Qi

2015-01-01

This study proposes a visualization processing method for the deformation risk level of underground space. The proposed method is based on a BP-Hopfield-RGB (BHR) composite network. Complex environmental factors are integrated in the BP neural network. Dynamic monitoring data are then automatically classified in the Hopfield network. The deformation risk level is combined with the RGB color space model and is displayed visually in real time, after which experiments are conducted with the use of an ultrasonic omnidirectional sensor device for structural deformation monitoring. The proposed method is also compared with some typical methods using a benchmark dataset. Results show that the BHR composite network visualizes the deformation monitoring process in real time and can dynamically indicate dangerous zones. PMID:26011618

Investigating the potential of employer-based "real-time" ridesharing.

DOT National Transportation Integrated Search

2015-01-01

The reemergence of ridesharing as a desirable means of travel is partly attributed to the role mobile phone and social networking technologies could play in enabling the real-time (or dynamic) matching of passengers and drivers producing ...

Real-Time Processing System for the JET Hard X-Ray and Gamma-Ray Profile Monitor Enhancement

NASA Astrophysics Data System (ADS)

Fernandes, Ana M.; Pereira, Rita C.; Neto, André; Valcárcel, Daniel F.; Alves, Diogo; Sousa, Jorge; Carvalho, Bernardo B.; Kiptily, Vasily; Syme, Brian; Blanchard, Patrick; Murari, Andrea; Correia, Carlos M. B. A.; Varandas, Carlos A. F.; Gonçalves, Bruno

2014-06-01

The Joint European Torus (JET) is currently undertaking an enhancement program which includes tests of relevant diagnostics with real-time processing capabilities for the International Thermonuclear Experimental Reactor (ITER). Accordingly, a new real-time processing system was developed and installed at JET for the gamma-ray and hard X-ray profile monitor diagnostic. The new system is connected to 19 CsI(Tl) photodiodes in order to obtain the line-integrated profiles of the gamma-ray and hard X-ray emissions. Moreover, it was designed to overcome the former data acquisition (DAQ) limitations while exploiting the required real-time features. The new DAQ hardware, based on the Advanced Telecommunication Computer Architecture (ATCA) standard, includes reconfigurable digitizer modules with embedded field-programmable gate array (FPGA) devices capable of acquiring and simultaneously processing data in real-time from the 19 detectors. A suitable algorithm was developed and implemented in the FPGAs, which are able to deliver the corresponding energy of the acquired pulses. The processed data is sent periodically, during the discharge, through the JET real-time network and stored in the JET scientific databases at the end of the pulse. The interface between the ATCA digitizers, the JET control and data acquisition system (CODAS), and the JET real-time network is provided by the Multithreaded Application Real-Time executor (MARTe). The work developed allowed attaining two of the major milestones required by next fusion devices: the ability to process and simultaneously supply high volume data rates in real-time.

Neural Network Classifies Teleoperation Data

NASA Technical Reports Server (NTRS)

Fiorini, Paolo; Giancaspro, Antonio; Losito, Sergio; Pasquariello, Guido

1994-01-01

Prototype artificial neural network, implemented in software, identifies phases of telemanipulator tasks in real time by analyzing feedback signals from force sensors on manipulator hand. Prototype is early, subsystem-level product of continuing effort to develop automated system that assists in training and supervising human control operator: provides symbolic feedback (e.g., warnings of impending collisions or evaluations of performance) to operator in real time during successive executions of same task. Also simplifies transition between teleoperation and autonomous modes of telerobotic system.

Methods of using real-time social media technologies for detection and remote monitoring of HIV outcomes.

PubMed

Young, Sean D; Rivers, Caitlin; Lewis, Bryan

2014-06-01

Recent availability of "big data" might be used to study whether and how sexual risk behaviors are communicated on real-time social networking sites and how data might inform HIV prevention and detection. This study seeks to establish methods of using real-time social networking data for HIV prevention by assessing 1) whether geolocated conversations about HIV risk behaviors can be extracted from social networking data, 2) the prevalence and content of these conversations, and 3) the feasibility of using HIV risk-related real-time social media conversations as a method to detect HIV outcomes. In 2012, tweets (N=553,186,061) were collected online and filtered to include those with HIV risk-related keywords (e.g., sexual behaviors and drug use). Data were merged with AIDSVU data on HIV cases. Negative binomial regressions assessed the relationship between HIV risk tweeting and prevalence by county, controlling for socioeconomic status measures. Over 9800 geolocated tweets were extracted and used to create a map displaying the geographical location of HIV-related tweets. There was a significant positive relationship (p<.01) between HIV-related tweets and HIV cases. Results suggest the feasibility of using social networking data as a method for evaluating and detecting Human immunodeficiency virus (HIV) risk behaviors and outcomes. Copyright © 2014 Elsevier Inc. All rights reserved.

Integrated modeling of storm drain and natural channel networks for real-time flash flood forecasting in large urban areas

NASA Astrophysics Data System (ADS)

Habibi, H.; Norouzi, A.; Habib, A.; Seo, D. J.

2016-12-01

To produce accurate predictions of flooding in urban areas, it is necessary to model both natural channel and storm drain networks. While there exist many urban hydraulic models of varying sophistication, most of them are not practical for real-time application for large urban areas. On the other hand, most distributed hydrologic models developed for real-time applications lack the ability to explicitly simulate storm drains. In this work, we develop a storm drain model that can be coupled with distributed hydrologic models such as the National Weather Service Hydrology Laboratory's Distributed Hydrologic Model, for real-time flash flood prediction in large urban areas to improve prediction and to advance the understanding of integrated response of natural channels and storm drains to rainfall events of varying magnitude and spatiotemporal extent in urban catchments of varying sizes. The initial study area is the Johnson Creek Catchment (40.1 km2) in the City of Arlington, TX. For observed rainfall, the high-resolution (500 m, 1 min) precipitation data from the Dallas-Fort Worth Demonstration Network of the Collaborative Adaptive Sensing of the Atmosphere radars is used.

Design of thrust vectoring exhaust nozzles for real-time applications using neural networks

NASA Technical Reports Server (NTRS)

Prasanth, Ravi K.; Markin, Robert E.; Whitaker, Kevin W.

1991-01-01

Thrust vectoring continues to be an important issue in military aircraft system designs. A recently developed concept of vectoring aircraft thrust makes use of flexible exhaust nozzles. Subtle modifications in the nozzle wall contours produce a non-uniform flow field containing a complex pattern of shock and expansion waves. The end result, due to the asymmetric velocity and pressure distributions, is vectored thrust. Specification of the nozzle contours required for a desired thrust vector angle (an inverse design problem) has been achieved with genetic algorithms. This approach is computationally intensive and prevents the nozzles from being designed in real-time, which is necessary for an operational aircraft system. An investigation was conducted into using genetic algorithms to train a neural network in an attempt to obtain, in real-time, two-dimensional nozzle contours. Results show that genetic algorithm trained neural networks provide a viable, real-time alternative for designing thrust vectoring nozzles contours. Thrust vector angles up to 20 deg were obtained within an average error of 0.0914 deg. The error surfaces encountered were highly degenerate and thus the robustness of genetic algorithms was well suited for minimizing global errors.

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.

High Resolution Sensing and Control of Urban Water Networks

NASA Astrophysics Data System (ADS)

Bartos, M. D.; Wong, B. P.; Kerkez, B.

2016-12-01

We present a framework to enable high-resolution sensing, modeling, and control of urban watersheds using (i) a distributed sensor network based on low-cost cellular-enabled motes, (ii) hydraulic models powered by a cloud computing infrastructure, and (iii) automated actuation valves that allow infrastructure to be controlled in real time. This platform initiates two major advances. First, we achieve a high density of measurements in urban environments, with an anticipated 40+ sensors over each urban area of interest. In addition to new measurements, we also illustrate the design and evaluation of a "smart" control system for real-world hydraulic networks. This control system improves water quality and mitigates flooding by using real-time hydraulic models to adaptively control releases from retention basins. We evaluate the potential of this platform through two ongoing deployments: (i) a flood monitoring network in the Dallas-Fort Worth metropolitan area that detects and anticipates floods at the level of individual roadways, and (ii) a real-time hydraulic control system in the city of Ann Arbor, MI—soon to be one of the most densely instrumented urban watersheds in the United States. Through these applications, we demonstrate that distributed sensing and control of water infrastructure can improve flash flood predictions, emergency response, and stormwater contaminant mitigation.

OR.NET RT: how service-oriented medical device architecture meets real-time communication.

PubMed

Pfeiffer, Jonas H; Kasparick, Martin; Strathen, Benjamin; Dietz, Christian; Dingler, Max E; Lueth, Tim C; Timmermann, Dirk; Radermacher, Klaus; Golatowski, Frank

2018-02-23

Today's landscape of medical devices is dominated by stand-alone systems and proprietary interfaces lacking cross-vendor interoperability. This complicates or even impedes the innovation of novel, intelligent assistance systems relying on the collaboration of medical devices. Emerging approaches use the service-oriented architecture (SOA) paradigm based on Internet protocol (IP) to enable communication between medical devices. While this works well for scenarios with no or only soft timing constraints, the underlying best-effort communication scheme is insufficient for time critical data. Real-time (RT) networks are able to reliably guarantee fixed latency boundaries, for example, by using time division multiple access (TDMA) communication patterns. However, deterministic RT networks come with their own limitations such as tedious, inflexible configuration and a more restricted bandwidth allocation. In this contribution we overcome the drawbacks of both approaches by describing and implementing mechanisms that allow the two networks to interact. We introduce the first implementation of a medical device network that offers hard RT guarantees for control and sensor data and integrates into SOA networks. Based on two application examples we show how the flexibility of SOA networks and the reliability of RT networks can be combined to achieve an open network infrastructure for medical devices in the operating room (OR).

The French contribution to the voluntary observing ships network of sea surface salinity

NASA Astrophysics Data System (ADS)

Alory, G.; Delcroix, T.; Téchiné, P.; Diverrès, D.; Varillon, D.; Cravatte, S.; Gouriou, Y.; Grelet, J.; Jacquin, S.; Kestenare, E.; Maes, C.; Morrow, R.; Perrier, J.; Reverdin, G.; Roubaud, F.

2015-11-01

Sea Surface Salinity (SSS) is an essential climate variable that requires long term in situ observation. The French SSS Observation Service (SSS-OS) manages a network of Voluntary Observing Ships equipped with thermosalinographs (TSG). The network is global though more concentrated in the tropical Pacific and North Atlantic oceanic basins. The acquisition system is autonomous with real time transmission and is regularly serviced at harbor calls. There are distinct real time and delayed time processing chains. Real time processing includes automatic alerts to detect potential instrument problems, in case raw data are outside of climatic limits, and graphical monitoring tools. Delayed time processing relies on a dedicated software for attribution of data quality flags by visual inspection, and correction of TSG time series by comparison with daily water samples and collocated Argo data. A method for optimizing the automatic attribution of quality flags in real time, based on testing different thresholds for data deviation from climatology and retroactively comparing the resulting flags to delayed time flags, is presented. The SSS-OS real time data feed the Coriolis operational oceanography database, while the research-quality delayed time data can be extracted for selected time and geographical ranges through a graphical web interface. Delayed time data have been also combined with other SSS data sources to produce gridded files for the Pacific and Atlantic oceans. A short review of the research activities conducted with such data is given. It includes observation-based process-oriented and climate studies from regional to global scale as well as studies where in situ SSS is used for calibration/validation of models, coral proxies or satellite data.

The French Contribution to the Voluntary Observing Ships Network of Sea Surface Salinity

NASA Astrophysics Data System (ADS)

Delcroix, T. C.; Alory, G.; Téchiné, P.; Diverrès, D.; Varillon, D.; Cravatte, S. E.; Gouriou, Y.; Grelet, J.; Jacquin, S.; Kestenare, E.; Maes, C.; Morrow, R.; Perrier, J.; Reverdin, G. P.; Roubaud, F.

2016-02-01

Sea Surface Salinity (SSS) is an essential climate variable that requires long term in situ observation. The French SSS Observation Service (SSS-OS) manages a network of Voluntary Observing Ships equipped with thermosalinographs (TSG). The network is global though more concentrated in the tropical Pacific and North Atlantic oceanic basins. The acquisition system is autonomous with real time transmission and is regularly serviced at harbor calls. There are distinct real time and delayed time processing chains. Real time processing includes automatic alerts to detect potential instrument problems, in case raw data are outside of climatic limits, and graphical monitoring tools. Delayed time processing relies on a dedicated software for attribution of data quality flags by visual inspection, and correction of TSG time series by comparison with daily water samples and collocated Argo data. A method for optimizing the automatic attribution of quality flags in real time, based on testing different thresholds for data deviation from climatology and retroactively comparing the resulting flags to delayed time flags, is presented. The SSS-OS real time data feed the Coriolis operational oceanography database, while the research-quality delayed time data can be extracted for selected time and geographical ranges through a graphical web interface. Delayed time data have been also combined with other SSS data sources to produce gridded files for the Pacific and Atlantic oceans. A short review of the research activities conducted with such data is given. It includes observation-based process-oriented and climate studies from regional to global scale as well as studies where in situ SSS is used for calibration/validation of models, coral proxies or satellite data.

Hybrid monitoring scheme for end-to-end performance enhancement of multicast-based real-time media

NASA Astrophysics Data System (ADS)

Park, Ju-Won; Kim, JongWon

2004-10-01

As real-time media applications based on IP multicast networks spread widely, end-to-end QoS (quality of service) provisioning for these applications have become very important. To guarantee the end-to-end QoS of multi-party media applications, it is essential to monitor the time-varying status of both network metrics (i.e., delay, jitter and loss) and system metrics (i.e., CPU and memory utilization). In this paper, targeting the multicast-enabled AG (Access Grid) a next-generation group collaboration tool based on multi-party media services, the applicability of hybrid monitoring scheme that combines active and passive monitoring is investigated. The active monitoring measures network-layer metrics (i.e., network condition) with probe packets while the passive monitoring checks both application-layer metrics (i.e., user traffic condition by analyzing RTCP packets) and system metrics. By comparing these hybrid results, we attempt to pinpoint the causes of performance degradation and explore corresponding reactions to improve the end-to-end performance. The experimental results show that the proposed hybrid monitoring can provide useful information to coordinate the performance improvement of multi-party real-time media applications.

Real-time operation of the NSF EarthScope USArray Transportable Array

NASA Astrophysics Data System (ADS)

Astiz, L.; Eakins, J. A.; Vernon, F. L.; Martynov, V.; Newman, R. L.; Cox, T. A.; Mulder, T. L.; Busby, R. W.

2007-05-01

The Transportable Array (TA) component of USArray uses real-time telemetry to send data to the Array Network Facility (ANF) through a variety of satellite, mobile phone, wireless and wired communication links. The ANF is responsible for the timely delivery of metadata and waveform data to the IRIS DMC from the growing number of Transportable Array stations. The IRIS DMC makes these data available to the research community. The network has increased in size to 327 stations with 259 out of the 400 new TA sites installed (as of 28 February 2007). Starting in Fall 2007, equipment will start to roll from current stations to new locations to the east of the current footprint. Use of the Antelope software package has allowed the ANF to maintain and operate this extremely dynamic network configuration, facilitating the collection and transfer of data, the generation and merging of the metadata as well as the real-time monitoring of state of health of TA station data-loggers and their command and control. Four regional networks (ANZA, BDSN, SCSN, and UNR) as well as the USNSN contribute data to the Transportable Array in real-time. Although the real-time data flow to the IRIS DMC has been 93.4% over the last year, the ANF and the TA field teams have extended every effort and have managed to recover an additional 4.8% by recovering data from the local data storage device (Baler 14) at each station. Once the missing data is recovered, we then generate station-channel-day volume seed files, which are resent to the DMC to bring the total data recovery rate to 98.4%. The total network uptime is above 99%. Analyst review of automatic locations for the USArray network is being done at the ANF as part of the data quality monitoring strategy. All events are associated with the USGS and regional network bulletins. As of February 2007, around 13,000 weekly picks are being fully reviewed by analysts at the ANF and over 19,000 events have been recorded. We find a small percentage (about 10 %) of events that cannot be associated with existing bulletins. This information is used by the regional network operators to help them determine which TA stations may be beneficial to permanently add to their seismic networks. Operation of the USArray at the ANF has benefited by the real-time interface with the ORB and the Datascope database using PHP for display on the ANF website (http:anf.ucsd.edu) to provide station and system state-of- health information to field teams. Information available for all stations includes: location, maps, photographs, equipment deployed, communications, distribution of events recorded by each station, and displays of daily, weekly, and yearly state of health parameters as well as station noise spectra generated by the DMC.

Real Time Updating Genetic Network Programming for Adapting to the Change of Stock Prices

NASA Astrophysics Data System (ADS)

Chen, Yan; Mabu, Shingo; Shimada, Kaoru; Hirasawa, Kotaro

The key in stock trading model is to take the right actions for trading at the right time, primarily based on the accurate forecast of future stock trends. Since an effective trading with given information of stock prices needs an intelligent strategy for the decision making, we applied Genetic Network Programming (GNP) to creating a stock trading model. In this paper, we propose a new method called Real Time Updating Genetic Network Programming (RTU-GNP) for adapting to the change of stock prices. There are three important points in this paper: First, the RTU-GNP method makes a stock trading decision considering both the recommendable information of technical indices and the candlestick charts according to the real time stock prices. Second, we combine RTU-GNP with a Sarsa learning algorithm to create the programs efficiently. Also, sub-nodes are introduced in each judgment and processing node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. Third, a Real Time Updating system has been firstly introduced in our paper considering the change of the trend of stock prices. The experimental results on the Japanese stock market show that the trading model with the proposed RTU-GNP method outperforms other models without real time updating. We also compared the experimental results using the proposed method with Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than Buy&Hold method.

A grid-based tropospheric product for China using a GNSS network

NASA Astrophysics Data System (ADS)

Zhang, Hongxing; Yuan, Yunbin; Li, Wei; Zhang, Baocheng; Ou, Jikun

2017-11-01

Tropospheric delay accounts for one source of error in global navigation satellite systems (GNSS). To better characterize the tropospheric delays in the temporal and spatial domain and facilitate the safety-critical use of GNSS across China, a method is proposed to generate a grid-based tropospheric product (GTP) using the GNSS network with an empirical tropospheric model, known as IGGtrop. The prototype system generates the GTPs in post-processing and real-time modes and is based on the undifferenced and uncombined precise point positioning (UU-PPP) technique. GTPs are constructed for a grid form (2.0{°}× 2.5{°} latitude-longitude) over China with a time resolution of 5 min. The real-time GTP messages are encoded in a self-defined RTCM3 format and broadcast to users using NTRIP (networked transport of RTCM via internet protocol), which enables efficient and safe transmission to real-time users. Our approach for GTP generation consists of three sequential steps. In the first step, GNSS-derived zenith tropospheric delays (ZTDs) for a network of GNSS stations are estimated using UU-PPP. In the second step, vertical adjustments for the GNSS-derived ZTDs are applied to address the height differences between the GNSS stations and grid points. The ZTD height corrections are provided by the IGGtrop model. Finally, an inverse distance weighting method is used to interpolate the GNSS-derived ZTDs from the surrounding GNSS stations to the location of the grid point. A total of 210 global positioning system (GPS) stations from the crustal movement observation network of China are used to generate the GTPs in both post-processing and real-time modes. The accuracies of the GTPs are assessed against with ERA-Interim-derived ZTDs and the GPS-derived ZTDs at 12 test GPS stations, respectively. The results show that the post-processing and real-time GTPs can provide the ZTDs with accuracies of 1.4 and 1.8 cm, respectively. We also apply the GTPs in real-time kinematic GPS PPP, and the results show that the convergence time of the PPP solutions is shortened. These results confirm that the GTPs can act as an efficient information source to augment GNSS positioning over China.

Earthquake early warning for Romania - most recent improvements

NASA Astrophysics Data System (ADS)

Marmureanu, Alexandru; Elia, Luca; Martino, Claudio; Colombelli, Simona; Zollo, Aldo; Cioflan, Carmen; Toader, Victorin; Marmureanu, Gheorghe; Marius Craiu, George; Ionescu, Constantin

2014-05-01

EWS for Vrancea earthquakes uses the time interval (28-32 sec.) between the moment when the earthquake is detected by the local seismic network installed in the epicenter area (Vrancea) and the arrival time of the seismic waves in the protected area (Bucharest) to send earthquake warning to users. In the last years, National Institute for Earth Physics (NIEP) upgraded its seismic network in order to cover better the seismic zones of Romania. Currently the National Institute for Earth Physics (NIEP) operates a real-time seismic network designed to monitor the seismic activity on the Romania territory, dominated by the Vrancea intermediate-depth (60-200 km) earthquakes. The NIEP real-time network consists of 102 stations and two seismic arrays equipped with different high quality digitizers (Kinemetrics K2, Quanterra Q330, Quanterra Q330HR, PS6-26, Basalt), broadband and short period seismometers (CMG3ESP, CMG40T, KS2000, KS54000, KS2000, CMG3T,STS2, SH-1, S13, Ranger, gs21, Mark l22) and acceleration sensors (Episensor). Recent improvement of the seismic network and real-time communication technologies allows implementation of a nation-wide EEWS for Vrancea and other seismic sources from Romania. We present a regional approach to Earthquake Early Warning for Romania earthquakes. The regional approach is based on PRESTo (Probabilistic and Evolutionary early warning SysTem) software platform: PRESTo processes in real-time three channel acceleration data streams: once the P-waves arrival have been detected, it provides earthquake location and magnitude estimations, and peak ground motion predictions at target sites. PRESTo is currently implemented in real- time at National Institute for Earth Physics, Bucharest for several months in parallel with a secondary EEWS. The alert notification is issued only when both systems validate each other. Here we present the results obtained using offline earthquakes originating from Vrancea area together with several real-time detection of significant earthquakes from Vrancea and Transylvania areas that occurred in the last months. Currently the warning notification is sent to several users including emergency response units from 12 counties, a big bridge located in Bucharest, a nuclear sterilization facility in Măgurele city and to the nuclear power plant from Cernavoda.

An optimized compression algorithm for real-time ECG data transmission in wireless network of medical information systems.

PubMed

Cho, Gyoun-Yon; Lee, Seo-Joon; Lee, Tae-Ro

2015-01-01

Recent medical information systems are striving towards real-time monitoring models to care patients anytime and anywhere through ECG signals. However, there are several limitations such as data distortion and limited bandwidth in wireless communications. In order to overcome such limitations, this research focuses on compression. Few researches have been made to develop a specialized compression algorithm for ECG data transmission in real-time monitoring wireless network. Not only that, recent researches' algorithm is not appropriate for ECG signals. Therefore this paper presents a more developed algorithm EDLZW for efficient ECG data transmission. Results actually showed that the EDLZW compression ratio was 8.66, which was a performance that was 4 times better than any other recent compression method widely used today.

US GEOLOGICAL SURVEY'S NATIONAL SYSTEM FOR PROCESSING AND DISTRIBUTION OF NEAR REAL-TIME HYDROLOGICAL DATA.

USGS Publications Warehouse

Shope, William G.; ,

1987-01-01

The US Geological Survey is utilizing a national network of more than 1000 satellite data-collection stations, four satellite-relay direct-readout ground stations, and more than 50 computers linked together in a private telecommunications network to acquire, process, and distribute hydrological data in near real-time. The four Survey offices operating a satellite direct-readout ground station provide near real-time hydrological data to computers located in other Survey offices through the Survey's Distributed Information System. The computerized distribution system permits automated data processing and distribution to be carried out in a timely manner under the control and operation of the Survey office responsible for the data-collection stations and for the dissemination of hydrological information to the water-data users.

Real Time Precise Point Positioning: Preliminary Results for the Brazilian Region

NASA Astrophysics Data System (ADS)

Marques, Haroldo; Monico, João.; Hirokazu Shimabukuro, Milton; Aquino, Marcio

2010-05-01

GNSS positioning can be carried out in relative or absolute approach. In the last years, more attention has been driven to the real time precise point positioning (PPP). To achieve centimeter accuracy with this method in real time it is necessary to have available the satellites precise coordinates as well as satellites clocks corrections. The coordinates can be used from the predicted IGU ephemeris, but the satellites clocks must be estimated in a real time. It can be made from a GNSS network as can be seen from EUREF Permanent Network. The infra-structure to realize the PPP in real time is being available in Brazil through the Brazilian Continuous Monitoring Network (RBMC) together with the Sao Paulo State GNSS network which are transmitting GNSS data using NTRIP (Networked Transport of RTCM via Internet Protocol) caster. Based on this information it was proposed a PhD thesis in the Univ. Estadual Paulista (UNESP) aiming to investigate and develop the methodology to estimate the satellites clocks and realize PPP in real time. Then, software is being developed to process GNSS data in the real time PPP mode. A preliminary version of the software was called PPP_RT and is able to process GNSS code and phase data using precise ephemeris and satellites clocks. The PPP processing can be accomplished considering the absolute satellite antenna Phase Center Variation (PCV), Ocean Tide Loading (OTL), Earth Body Tide, among others. The first order ionospheric effects can be eliminated or minimized by ion-free combination or parameterized in the receiver-satellite direction using a stochastic process, e.g. random walk or white noise. In the case of ionosphere estimation, a pseudo-observable is introduced in the mathematical model for each satellite and the initial value can be computed from Klobuchar model or from Global Ionospheric Map (GIM). The adjustment is realized in the recursive mode and the DIA (Detection Identification and Adaptation) is used for quality control. In this paper our proposition is to present the mathematical models implemented in the PPP_RT software and some proposal to accomplish the PPP in real time as for example using tropospheric model from Brazilian Numerical Weather Forecast Model (BNWFM) and estimating the ionosphere using stochastic process. GPS data sample from the Brazilian region was processed using the PPP_RT software considering periods under low and high ionospheric activities and the results estimating the ionosphere were compared with the ion-free combination. The PPP results also were analyzed considering the strategy of the troposphere estimation, Hopfield model or using the BNWFM. For the troposphere case, the values from BNWFM can reach similar results when estimating the troposphere. For the ionosphere case, the results have shown that ionosphere estimation can improve the time convergence of the PPP processing what is very important for PPP in real time.

Coverage centralities for temporal networks*

NASA Astrophysics Data System (ADS)

Takaguchi, Taro; Yano, Yosuke; Yoshida, Yuichi

2016-02-01

Structure of real networked systems, such as social relationship, can be modeled as temporal networks in which each edge appears only at the prescribed time. Understanding the structure of temporal networks requires quantifying the importance of a temporal vertex, which is a pair of vertex index and time. In this paper, we define two centrality measures of a temporal vertex based on the fastest temporal paths which use the temporal vertex. The definition is free from parameters and robust against the change in time scale on which we focus. In addition, we can efficiently compute these centrality values for all temporal vertices. Using the two centrality measures, we reveal that distributions of these centrality values of real-world temporal networks are heterogeneous. For various datasets, we also demonstrate that a majority of the highly central temporal vertices are located within a narrow time window around a particular time. In other words, there is a bottleneck time at which most information sent in the temporal network passes through a small number of temporal vertices, which suggests an important role of these temporal vertices in spreading phenomena. Contribution to the Topical Issue "Temporal Network Theory and Applications", edited by Petter Holme.Supplementary material in the form of one pdf file available from the Journal web page at http://dx.doi.org/10.1140/epjb/e2016-60498-7

Development of a neural network technique for KSTAR Thomson scattering diagnostics

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

Lee, Seung Hun, E-mail: leesh81@nfri.re.kr; Lee, J. H.; Yamada, I.

Neural networks provide powerful approaches of dealing with nonlinear data and have been successfully applied to fusion plasma diagnostics and control systems. Controlling tokamak plasmas in real time is essential to measure the plasma parameters in situ. However, the χ{sup 2} method traditionally used in Thomson scattering diagnostics hampers real-time measurement due to the complexity of the calculations involved. In this study, we applied a neural network approach to Thomson scattering diagnostics in order to calculate the electron temperature, comparing the results to those obtained with the χ{sup 2} method. The best results were obtained for 10{sup 3} training cyclesmore » and eight nodes in the hidden layer. Our neural network approach shows good agreement with the χ{sup 2} method and performs the calculation twenty times faster.« less

Venture Evaluation and Review Technique (VERT). Users’/Analysts’ Manual

DTIC Science & Technology

1979-10-01

real world. Additionally, activity pro- cessing times could be entered as a normal, uniform or triangular distribution. Activity times can also be...work or tasks, or if the unit activities are such abstractions of the real world that the estimation of the time , cost and performance parameters for...utilized in that con- straining capacity. 7444 The network being processed has passed all the previous error checks. It currently has a real time

Freight information real-time system for transport (FIRST)

DOT National Transportation Integrated Search

2002-05-01

The FIRST Demonstration Project was funded and developed, in part, to provide unique solutions to freight transportation problems. FIRST is an Internet-based, real-time network that integrates many resources into a single, easy-to-use Web site on car...

Social Network Analysis of Crowds

DTIC Science & Technology

2009-10-29

Response to non-lethal weapons fire depends on social relationships among crowd members – Pre-existing Personal Relationships – Ongoing Real Time...weapons and systems – Prior, existing social relationships – Real time social interactions – Formal/informal hierarchies 13-Feb-15 24UNCLASSIFIED

The INTELSAT VI SSTDMA network diagnostic system

NASA Astrophysics Data System (ADS)

Tamboli, Satish P.; Zhu, Xiaobo; Wilkins, Kim N.; Gupta, Ramesh K.

The system-level design of an expert-system-based, near-real-time diagnostic system for INTELSAT VI satellite-switched time-division multiple access (SSTDMA) network is described. The challenges of INTELSAT VI diagnostics are discussed, along with alternative approaches for network diagnostics and the rationale for choosing a method based on burst unique-word detection. The focal point of the diagnostic system is the diagnostic processor, which resides in the central control and monitoring facility known as the INTELSAT Operations Center TDMA Facility (IOCTF). As real-time information such as burst unique-word detection data, reference terminal status data, and satellite telemetry alarm data are received at the IOCTF, the diagnostic processor continuously monitors the data streams. When a burst status change is detected, a 'snapshot' of the real-time data is forwarded to the expert system. Receipt of the change causes a set of rules to be invoked which associate the traffic pattern with a set of probable causes. A user-friendly interface allows a graphical view of the burst time plan and provides the ability to browse through the knowledge bases.

Real Time GPS- Satellite Clock Estimation Development of a RTIGS Web Service

NASA Astrophysics Data System (ADS)

Opitz, M.; Weber, R.; Caissy, M.

2006-12-01

Since 3 years the IGS (International GNSS Service) Real-Time Working Group disseminates via Internet raw observation data of a subset of stations of the IGS network. This observation data can be used to establish a real-time integrity monitoring of the IGS predicted orbits (Ultra Rapid (IGU-) Orbits) and clocks, according to the recommendations of the IGS Workshop 2004 in Bern. The Institute for "Geodesy and Geophysics" of the TU-Vienna develops in cooperation with the IGS Real-Time Working Group the software "RTR- Control", which currently provides a real-time integrity monitoring of predicted IGU Clock Corrections to GPS Time. Our poster presents the results of a prototype version which is in operation since August this year. Besides RTR-Control allows for the comparison of pseudoranges measured at any permanent station in the global network with theoretical pseudoranges calculated on basis of the IGU- orbits. Thus, the programme can diagnose incorrectly predicted satellite orbits and clocks as well as detect multi-path distorted pseudoranges in real- time. RTR- Control calculates every 15 seconds Satellite Clock Corrections with respect to the most recent IGU- clocks (updated in a 6 hours interval). The clock estimations are referenced to a stable station clock (H-maser) with a small offset to GPS- time. This real-time Satellite Clocks are corrected for individual outliers and modelling errors. The most recent GPS- Satellite Clock Corrections (updated every 60 seconds) are published in Real Time via the Internet. The user group interested in a rigorous integrity monitoring comprises on the one hand the components of IGS itself to qualify the issued orbital data and on the other hand all users of the IGS Ultra Rapid Products (e.g. for PPP in Real Time).

Normal streamflows and water levels continue—Summary of hydrologic conditions in Georgia, 2014

USGS Publications Warehouse

Knaak, Andrew E.; Ankcorn, Paul D.; Peck, Michael F.

2016-03-31

The U.S. Geological Survey (USGS) South Atlantic Water Science Center (SAWSC) Georgia office, in cooperation with local, State, and other Federal agencies, maintains a long-term hydrologic monitoring network of more than 350 real-time, continuous-record, streamflow-gaging stations (streamgages). The network includes 14 real-time lake-level monitoring stations, 72 real-time surface-water-quality monitors, and several water-quality sampling programs. Additionally, the SAWSC Georgia office operates more than 204 groundwater monitoring wells, 39 of which are real-time. The wide-ranging coverage of streamflow, reservoir, and groundwater monitoring sites allows for a comprehensive view of hydrologic conditions across the State. One of the many benefits this monitoring network provides is a spatially distributed overview of the hydrologic conditions of creeks, rivers, reservoirs, and aquifers in Georgia.Streamflow and groundwater data are verified throughout the year by USGS hydrographers and made available to water-resource managers, recreationists, and Federal, State, and local agencies. Hydrologic conditions are determined by comparing the statistical analyses of data collected during the current water year to historical data. Changing hydrologic conditions underscore the need for accurate, timely data to allow informed decisions about the management and conservation of Georgia’s water resources for agricultural, recreational, ecological, and water-supply needs and in protecting life and property.

High Resolution Flash Flood Forecasting Using a Wireless Sensor Network in the Dallas-Fort Worth Metroplex

NASA Astrophysics Data System (ADS)

Bartos, M. D.; Kerkez, B.; Noh, S.; Seo, D. J.

2017-12-01

In this study, we develop and evaluate a high resolution urban flash flood monitoring system using a wireless sensor network (WSN), a real-time rainfall-runoff model, and spatially-explicit radar rainfall predictions. Flooding is the leading cause of natural disaster fatalities in the US, with flash flooding in particular responsible for a majority of flooding deaths. While many riverine flood models have been operationalized into early warning systems, there is currently no model that is capable of reliably predicting flash floods in urban areas. Urban flash floods are particularly difficult to model due to a lack of rainfall and runoff data at appropriate scales. To address this problem, we develop a wide-area flood-monitoring wireless sensor network for the Dallas-Fort Worth metroplex, and use this network to characterize rainfall-runoff response over multiple heterogeneous catchments. First, we deploy a network of 22 wireless sensor nodes to collect real-time stream stage measurements over catchments ranging from 2-80 km2 in size. Next, we characterize the rainfall-runoff response of each catchment by combining stream stage data with gage and radar-based precipitation measurements. Finally, we demonstrate the potential for real-time flash flood prediction by joining the derived rainfall-runoff models with real-time radar rainfall predictions. We find that runoff response is highly heterogeneous among catchments, with large variabilities in runoff response detected even among nearby gages. However, when spatially-explicit rainfall fields are included, spatial variability in runoff response is largely captured. This result highlights the importance of increased spatial coverage for flash flood prediction.

Recursive Bayesian recurrent neural networks for time-series modeling.

PubMed

Mirikitani, Derrick T; Nikolaev, Nikolay

2010-02-01

This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.

Design of cold chain logistics remote monitoring system based on ZigBee and GPS location

NASA Astrophysics Data System (ADS)

Zong, Xiaoping; Shao, Heling

2017-03-01

This paper designed a remote monitoring system based on Bee Zig wireless sensor network and GPS positioning, according to the characteristics of cold chain logistics. The system consisted of the ZigBee network, gateway and monitoring center. ZigBee network temperature acquisition modules and GPS positioning acquisition module were responsible for data collection, and then send the data to the host computer through the GPRS network and Internet to realize remote monitoring of vehicle with functions of login permissions, temperature display, latitude and longitude display, historical data, real-time alarm and so on. Experiments showed that the system is stable, reliable and effective to realize the real-time remote monitoring of the vehicle in the process of cold chain transport.

Emergency seismic and CGPS networks: a first employment for the L'Aquila Mw 6.3 earthquake

NASA Astrophysics Data System (ADS)

Abruzzese, L.; Avallone, A.; Cecere, G.; Cattaneo, M.; Cardinale, V.; Castagnozzi, A.; Cogliano, R.; Criscuoli, F.; D'Agostino, N.; D'Ambrosio, C.; de Luca, G.; D'Anastasio, E.; Falco, L.; Flammia, V.; Migliari, F.; Minichiello, F.; Memmolo, A.; Monachesi, G.; Moschillo, R.; Pignone, M.; Pucillo, S.; Selvaggi, G.; Zarrilli, L.; Delladio, A.; Govoni, A.; Franceschi, D.; de Martin, M.; Moretti, M.

2009-12-01

During the last 2 years, the Istituto Nazionale di Geofisica e Vulcanologia (INGV) developed an important real-time temporary seismic network infrastructure in order to densify the Italian National Seismic Network in epicentral areas thus enhancing the localization of the micro-seismicity after main earthquake events. This real-time temporary seismic network is constituted by various mobile and autonomous seismic stations that in group of three are telemetered to a Very Small Aperture Terminal (VSAT). This system uses a dedicated bandwidth on UHF, Wi-Fi and satellite frequency that allows the data flow in real-time at INGV centre in Rome (and Grottaminarda as backup center). The deployment of the seismic network is managed in a geographical information systems (GIS) by particular scenarios that visualizes, for the epicentral area, information about instrumental seismicity, seismic risk, macroseismic felts and territorial data. Starting from digital terrain model, the surface spatial analysis (Viewshed, Observer Point) allows the geographic arrangement of the stations and relative scenarios. The April, 6th, 2009 Mw 6.3 L'Aquila destructive earthquake represented the first real-case to test the entire emergency seismic network infrastructure. Less than 6 hours after the earthquake occurrence, a first accelerometer station was already sending data at INGV seismic monitoring headquarters. A total number of 9 seismic stations have been installed within 3 days after the earthquake. Furthermore, 5 permanent GPS stations have been installed in the epicentral area within 1 to 9 days after the main shock to detect the post-seismic deformation induced by the earthquake. We will show and describe the details of the Emergency Seismic Network infrastructure, and the first results from the collected data.

Real-time earthquake monitoring: Early warning and rapid response

NASA Technical Reports Server (NTRS)

1991-01-01

A panel was established to investigate the subject of real-time earthquake monitoring (RTEM) and suggest recommendations on the feasibility of using a real-time earthquake warning system to mitigate earthquake damage in regions of the United States. The findings of the investigation and the related recommendations are described in this report. A brief review of existing real-time seismic systems is presented with particular emphasis given to the current California seismic networks. Specific applications of a real-time monitoring system are discussed along with issues related to system deployment and technical feasibility. In addition, several non-technical considerations are addressed including cost-benefit analysis, public perceptions, safety, and liability.

Real-Time IRI driven by GIRO data

NASA Astrophysics Data System (ADS)

Galkin, Ivan; Huang, Xueqin; Reinisch, Bodo; Bilitza, Dieter; Vesnin, Artem

Real-time extensions of the empirical International Reference Ionosphere (IRI) model are based on assimilative techniques that preserve the IRI formalism which is optimized for the description of climatological ionospheric features. The Global Ionosphere Radio Observatory (GIRO) team has developed critical parts of an IRI Real Time Assimilative Model (IRTAM) for the global ionospheric plasma distribution using measured data available in real time from ~50 ionosondes of the GIRO network, The current assimilation results present global assimilative maps of foF2 and hmF2 that reproduce available data at the sensor sites and smoothly return to the climatological specifications when and where the data are missing, and are free from artificial sharp gradients and short-lived artifacts when viewed in time progression. Animated real-time maps of foF2 and hmF2 are published with a few minutes latency at http://giro.uml.edu/IRTAM/. Our real-time IRI modeling uses morphing, a technique that transforms the climatological ionospheric specifications to match the observations by iteratively computing corrections to the original coefficients of the diurnal/spatial expansions, used in IRI to map the key ionospheric characteristics, while keeping the IRI expansion basis formalism intact. Computation of the updated coefficient set for a given point in time includes analysis of the latest 24-hour history of observations, which allows the morphing technique to sense evolving ionospheric dynamics even with a sparse sensor network. A Non-linear Error Compensation Technique for Associative Restoration (NECTAR), one of the features in our morphing approach, has been in operation at the Lowell GIRO Data Center since 2013. The cornerstone of NECTAR is a recurrent neural network optimizer that is responsible for smoothing the transitions between the grid cells where observations are available. NECTAR has proved suitable for real-time operations that require the assimilation code to be considerate of data uncertainties (noise) and immune to data errors. Future IRTAM work is directed toward accepting a greater diversity of near-real-time sensor data, and the paper discusses potential new data sources and challenges associated with their assimilation.

A study on the operation analysis of the power conditioning system with real HTS SMES coil

NASA Astrophysics Data System (ADS)

Kim, A. R.; Jung, H. Y.; Kim, J. H.; Ali, Mohd. Hasan; Park, M.; Yu, I. K.; Kim, H. J.; Kim, S. H.; Seong, K. C.

2008-09-01

Voltage sag from sudden increasing loads is one of the major problems in the utility network. In order to compensate the voltage sag problem, power compensation devices have widely been developed. In the case of voltage sag, it needs an energy source to overcome the energy caused by voltage sag. According as the SMES device is characterized by its very high response time of charge and discharge, it has widely been researched and developed for more than 20 years. However, before the installation of SMES into utility, the system analysis has to be carried out with a certain simulation tool. This paper presents a real-time simulation algorithm for the SMES system by using the miniaturized SMES model coil whose properties are same as those of real size SMES coil. With this method, researchers can easily analyse the performance of SMES connected into utility network by abstracting the properties from the real modeled SMES coil and using the virtual simulated power network in RSCAD/RTDS.

A fast community detection method in bipartite networks by distance dynamics

NASA Astrophysics Data System (ADS)

Sun, Hong-liang; Ch'ng, Eugene; Yong, Xi; Garibaldi, Jonathan M.; See, Simon; Chen, Duan-bing

2018-04-01

Many real bipartite networks are found to be divided into two-mode communities. In this paper, we formulate a new two-mode community detection algorithm BiAttractor. It is based on distance dynamics model Attractor proposed by Shao et al. with extension from unipartite to bipartite networks. Since Jaccard coefficient of distance dynamics model is incapable to measure distances of different types of vertices in bipartite networks, our main contribution is to extend distance dynamics model from unipartite to bipartite networks using a novel measure Local Jaccard Distance (LJD). Furthermore, distances between different types of vertices are not affected by common neighbors in the original method. This new idea makes clear assumptions and yields interpretable results in linear time complexity O(| E |) in sparse networks, where | E | is the number of edges. Experiments on synthetic networks demonstrate it is capable to overcome resolution limit compared with existing other methods. Further research on real networks shows that this model can accurately detect interpretable community structures in a short time.

PRAIS: Distributed, real-time knowledge-based systems made easy

NASA Technical Reports Server (NTRS)

Goldstein, David G.

1990-01-01

This paper discusses an architecture for real-time, distributed (parallel) knowledge-based systems called the Parallel Real-time Artificial Intelligence System (PRAIS). PRAIS strives for transparently parallelizing production (rule-based) systems, even when under real-time constraints. PRAIS accomplishes these goals by incorporating a dynamic task scheduler, operating system extensions for fact handling, and message-passing among multiple copies of CLIPS executing on a virtual blackboard. This distributed knowledge-based system tool uses the portability of CLIPS and common message-passing protocols to operate over a heterogeneous network of processors.

A secure mobile multimedia system to assist emergency response teams.

PubMed

Belala, Yacine; Issa, Omneya; Gregoire, Jean-Charles; Wong, James

2008-08-01

Long wait times after injury and greater distances to travel between accident scenes and medical facilities contribute to increased, possibly unnecessary deaths. This paper describes a mobile emergency system aimed at reducing mortality by improving the readiness of hospital personnel, therefore allowing for more efficient treatment procedures to be performed when the victim arrives. The system is designed to provide a secure transmission of voice, medical data, and video in real-time over third-generation cellular networks. Test results obtained on a commercial network under real-life conditions demonstrate the ability to effectively transmit medical data over 3G networks, making them a viable option available to healthcare professionals.

Fieldservers and Sensor Service Grid as Real-time Monitoring Infrastructure for Ubiquitous Sensor Networks

PubMed Central

Honda, Kiyoshi; Shrestha, Aadit; Witayangkurn, Apichon; Chinnachodteeranun, Rassarin; Shimamura, Hiroshi

2009-01-01

The fieldserver is an Internet based observation robot that can provide an outdoor solution for monitoring environmental parameters in real-time. The data from its sensors can be collected to a central server infrastructure and published on the Internet. The information from the sensor network will contribute to monitoring and modeling on various environmental issues in Asia, including agriculture, food, pollution, disaster, climate change etc. An initiative called Sensor Asia is developing an infrastructure called Sensor Service Grid (SSG), which integrates fieldservers and Web GIS to realize easy and low cost installation and operation of ubiquitous field sensor networks. PMID:22574018

Photonic Network R&D Activities in Japan-Current Activities and Future Perspectives

NASA Astrophysics Data System (ADS)

Kitayama, Ken-Ichi; Miki, Tetsuya; Morioka, Toshio; Tsushima, Hideaki; Koga, Masafumi; Mori, Kazuyuki; Araki, Soichiro; Sato, Ken-Ichi; Onaka, Hiroshi; Namiki, Shu; Aoyama, Tomonori

2005-10-01

R&D activities on photonic networks in Japan are presented. First, milestones in current ongoing R&D programs supported by Japanese government agencies are introduced, including long-distance and wavelength division multiplexing (WDM) fiber transmission, wavelength routing, optical burst switching (OBS), and control-plane technology for IP backbone networks. Their goal was set to evolve a legacy telecommunications network to IP-over-WDM networks by introducing technologies for WDM and wavelength routing. We then discuss the perspectives of so-called PHASE II R&D programs for photonic networks over the next 5 years until 2010, by focusing on the report that has been recently issued by the Photonic Internet Forum (PIF), a consortium that has major carriers, telecom vendors, and Japanese academics as members. The PHASE II R&D programs should serve to establish a photonic platform to provide abundant bandwidth on demand, at any time on a real-time basis, through the customer's initiative to promote bandwidth-rich applications, such as grid computing, real-time digital-cinema streaming, medical and educational applications, and network storage in e-commerce.

Photonic network R and D activities in Japan

NASA Astrophysics Data System (ADS)

Kitayama, Ken-ichi; Miki, Tetsuya; Morioka, Toshio; Tsushima, Hideaki; Koga, Masafumi; Mori, Kazuyuki; Araki, Soichiro; Sato, Ken-ichi; Onaka, Hiroshi; Namiki, Shu; Aovama, Tomonori

2005-11-01

R and D activities on photonic networks in Japan are presented. First, milestones in current, ongoing R and D programs supported by Japanese government agencies are introduced, including long-distance and WDM fiber transmission, wavelength routing, optical burst switching, and control plane technology for IP backbone networks. Their goal was set to evolve a legacy telecommunications network to IP over WDM networks by introducing technologies for WDM and wavelength routing. We then discuss the perspectives of so-called PHASE II R and D programs for photonic networks over the next five years until 2010, by focusing on the report which has been recently issued by the Photonic Internet Forum (PIF), a consortium that has major carriers, telecom vendors, and Japanese academics as members. The PHASE II R and D programs should serve to establish a photonic platform to provide abundant bandwidth on demand, at any time on a real-time basis through the customer's initiative, to promote bandwidth-rich applications, such as grid computing, real-time digital-cinema streaming, medical and educational applications, and network storage in e-commerce.

Memristive Computational Architecture of an Echo State Network for Real-Time Speech Emotion Recognition

DTIC Science & Technology

2015-05-28

recognition is simpler and requires less computational resources compared to other inputs such as facial expressions . The Berlin database of Emotional ...Processing Magazine, IEEE, vol. 18, no. 1, pp. 32– 80, 2001. [15] K. R. Scherer, T. Johnstone, and G. Klasmeyer, “Vocal expression of emotion ...Network for Real-Time Speech- Emotion Recognition 5a. CONTRACT NUMBER IN-HOUSE 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 62788F 6. AUTHOR(S) Q

A High Speed Mobile Courier Data Access System That Processes Database Queries in Real-Time

NASA Astrophysics Data System (ADS)

Gatsheni, Barnabas Ndlovu; Mabizela, Zwelakhe

A secure high-speed query processing mobile courier data access (MCDA) system for a Courier Company has been developed. This system uses the wireless networks in combination with wired networks for updating a live database at the courier centre in real-time by an offsite worker (the Courier). The system is protected by VPN based on IPsec. There is no system that we know of to date that performs the task for the courier as proposed in this paper.

Method for neural network control of motion using real-time environmental feedback

NASA Technical Reports Server (NTRS)

Buckley, Theresa M. (Inventor)

1997-01-01

A method of motion control for robotics and other automatically controlled machinery using a neural network controller with real-time environmental feedback. The method is illustrated with a two-finger robotic hand having proximity sensors and force sensors that provide environmental feedback signals. The neural network controller is taught to control the robotic hand through training sets using back- propagation methods. The training sets are created by recording the control signals and the feedback signal as the robotic hand or a simulation of the robotic hand is moved through a representative grasping motion. The data recorded is divided into discrete increments of time and the feedback data is shifted out of phase with the control signal data so that the feedback signal data lag one time increment behind the control signal data. The modified data is presented to the neural network controller as a training set. The time lag introduced into the data allows the neural network controller to account for the temporal component of the robotic motion. Thus trained, the neural network controlled robotic hand is able to grasp a wide variety of different objects by generalizing from the training sets.

A study of internet of things real-time data updating based on WebSocket

NASA Astrophysics Data System (ADS)

Wei, Shoulin; Yu, Konglin; Dai, Wei; Liang, Bo; Zhang, Xiaoli

2015-12-01

The Internet of Things (IoT) is gradually entering the industrial stage. Web applications in IoT such as monitoring, instant messaging, real-time quote system changes need to be transmitted in real-time mode to client without client constantly refreshing and sending the request. These applications often need to be as fast as possible and provide nearly real-time components. Real-time data updating is becoming the core part of application layer visualization technology in IoT. With support of data push in server-side, running state of "Things" in IoT could be displayed in real-time mode. This paper discusses several current real-time data updating method and explores the advantages and disadvantages of each method. We explore the use of WebSocket in a new approach for real-time data updating in IoT, since WebSocket provides low delay, low network throughput solutions for full-duplex communication.

Network Inference via the Time-Varying Graphical Lasso

PubMed Central

Hallac, David; Park, Youngsuk; Boyd, Stephen; Leskovec, Jure

2018-01-01

Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements. In order to spot trends, detect anomalies, and interpret the temporal dynamics of such data, it is essential to understand the relationships between the different entities and how these relationships evolve over time. In this paper, we introduce the time-varying graphical lasso (TVGL), a method of inferring time-varying networks from raw time series data. We cast the problem in terms of estimating a sparse time-varying inverse covariance matrix, which reveals a dynamic network of interdependencies between the entities. Since dynamic network inference is a computationally expensive task, we derive a scalable message-passing algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in an efficient way. We also discuss several extensions, including a streaming algorithm to update the model and incorporate new observations in real time. Finally, we evaluate our TVGL algorithm on both real and synthetic datasets, obtaining interpretable results and outperforming state-of-the-art baselines in terms of both accuracy and scalability. PMID:29770256

Field Installation and Real-Time Data Processing of the New Integrated SeismoGeodetic System with Real-Time Acceleration and Displacement Measurements for Earthquake Characterization Based on High-Rate Seismic and GPS Data

NASA Astrophysics Data System (ADS)

Zimakov, Leonid; Jackson, Michael; Passmore, Paul; Raczka, Jared; Alvarez, Marcos; Barrientos, Sergio

2015-04-01

We will discuss and show the results obtained from an integrated SeismoGeodetic System, model SG160-09, installed in the Chilean National Network. The SG160-09 provides the user high rate GNSS and accelerometer data, full epoch-by-epoch measurement integrity and, using the Trimble Pivot™ SeismoGeodetic App, the ability to create combined GNSS and accelerometer high-rate (200Hz) displacement time series in real-time. The SG160-09 combines seismic recording with GNSS geodetic measurement in a single compact, ruggedized package. The system includes a low-power, 220-channel GNSS receiver powered by the latest Trimble-precise Maxwell™6 technology and supports tracking GPS, GLONASS and Galileo signals. The receiver incorporates on-board GNSS point positioning using Real-Time Precise Point Positioning (PPP) technology with satellite clock and orbit corrections delivered over IP networks. The seismic recording element includes an ANSS Class A, force balance triaxial accelerometer with the latest, low power, 24-bit A/D converter, which produces high-resolution seismic data. The SG160-09 processor acquires and packetizes both seismic and geodetic data and transmits it to the central station using an advanced, error-correction protocol with back fill capability providing data integrity between the field and the processing center. The SG160-09 has been installed in the seismic station close to the area of the Iquique earthquake of April 1, 2014, in northern Chile, a seismically prone area at the current time. The hardware includes the SG160-09 system, external Zephyr Geodetic-2 GNSS antenna, and high-speed Internet communication media. Both acceleration and displacement data was transmitted in real-time to the National Seismological Center in Santiago for real-time data processing using Earthworm / Early Bird software. Command/Control of the field station and real-time GNSS position correction are provided via the Pivot software suite. Data from the SG160-09 system was used for seismic event characterization along with data from traditional stand-alone broadband seismic and geodetic stations installed in the network. Our presentation will focus on the key improvements of the network installation with the SG160-09 system, rapid data transmission, and real-time data processing for strong seismic events and aftershock characterization as well as advanced features of the SG160-09 for Earthquake and Tsunami Early Warning system.

Transforming NAD 27 and NAD 83 positions : making legacy mapping and surveys GPS compatible.

DOT National Transportation Integrated Search

2015-06-01

The Connecticut Department of Transportation (CTDOT) and the University of Connecticut are creating a real-time network (RTN) to make real-time surveying widely available in Connecticut. This RTN uses global navigation satellite system (GNSS) technol...

Acquiring data in real time in Italy from the Antarctic Seismographic Argentinean Italian Network (ASAIN): testing the global capabilities of the EarthWorm and Antelope software suites.

NASA Astrophysics Data System (ADS)

Percy Plasencia Linares, Milton; Russi, Marino; Pesaresi, Damiano; Cravos, Claudio

2010-05-01

The Italian National Institute for Oceanography and Experimental Geophysics (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, OGS) is running the Antarctic Seismographic Argentinean Italian Network (ASAIN), made of 7 seismic stations located in the Scotia Sea region in Antarctica and in Tierra del Fuego - Argentina: data from these stations are transferred in real time to the OGS headquarters in Trieste (Italy) via satellite links provided by the Instituto Antártico Argentino (IAA). Data is collected and archived primarily in Güralp Compress Format (GCF) through the Scream! software at OGS and IAA, and transmitted also in real time to the Observatories and Research Facilities for European Seismology (ORFEUS). The main real time seismic data acquisition and processing system of the ASAIN network is based on the EarthWorm 7.3 (Open Source) software suite installed on a Linux server at the OGS headquarters in Trieste. It runs several software modules for data collection, data archiving, data publication on dedicated web servers: wave_serverV, Winston Wave Server, and data analysis and realtime monitoring through Swarm program. OGS is also running, in close cooperation with the Friuli-Venezia Giulia Civil Defense, the North East (NI) Italy seismic network, making use of the Antelope commercial software suite from BRTT as the main acquisition system. As a test to check the global capabilities of the Antelope software suite, we also set up an instance of Antelope acquiring data in real time from both the regional ASAIN seismic network in Antarctica and a subset of the Global Seismic Network (GSN) funded by the Incorporated Research Institution for Seismology (IRIS). The facilities of the IRIS Data Management System, and specifically the IRIS Data Management Center, were used for real time access to waveform required in this study. The first tests indicated that more than 80% of the earthquakes with magnitude M>5.0 listed in the Preliminary Determination of Epicenters (PDE) catalogue of the National Earthquake Information Center (NEIC) of the United States Geological Survey (USGS) were also correctly automatically detected by Antelope, with an average location error of 0.05 degrees and average body wave magnitude Mb estimation error below 0.1. The average time difference between event origin time and the actual time of event determination by Antelope was of about 45': the comparison with 20', the IASPEI91 P-wave travel time for 180 degrees distance, and 25', the estimate of our test system data latency, indicate that Antelope is a serious candidate for regional and global early warning systems.

Continuous high speed coherent one-way quantum key distribution.

PubMed

Stucki, Damien; Barreiro, Claudio; Fasel, Sylvain; Gautier, Jean-Daniel; Gay, Olivier; Gisin, Nicolas; Thew, Rob; Thoma, Yann; Trinkler, Patrick; Vannel, Fabien; Zbinden, Hugo

2009-08-03

Quantum key distribution (QKD) is the first commercial quantum technology operating at the level of single quanta and is a leading light for quantum-enabled photonic technologies. However, controlling these quantum optical systems in real world environments presents significant challenges. For the first time, we have brought together three key concepts for future QKD systems: a simple high-speed protocol; high performance detection; and integration both, at the component level and for standard fibre network connectivity. The QKD system is capable of continuous and autonomous operation, generating secret keys in real time. Laboratory and field tests were performed and comparisons made with robust InGaAs avalanche photodiodes and superconducting detectors. We report the first real world implementation of a fully functional QKD system over a 43 dB-loss (150 km) transmission line in the Swisscom fibre optic network where we obtained average real-time distribution rates over 3 hours of 2.5 bps.

Dynamical jumping real-time fault-tolerant routing protocol for wireless sensor networks.

PubMed

Wu, Guowei; Lin, Chi; Xia, Feng; Yao, Lin; Zhang, He; Liu, Bing

2010-01-01

In time-critical wireless sensor network (WSN) applications, a high degree of reliability is commonly required. A dynamical jumping real-time fault-tolerant routing protocol (DMRF) is proposed in this paper. Each node utilizes the remaining transmission time of the data packets and the state of the forwarding candidate node set to dynamically choose the next hop. Once node failure, network congestion or void region occurs, the transmission mode will switch to jumping transmission mode, which can reduce the transmission time delay, guaranteeing the data packets to be sent to the destination node within the specified time limit. By using feedback mechanism, each node dynamically adjusts the jumping probabilities to increase the ratio of successful transmission. Simulation results show that DMRF can not only efficiently reduce the effects of failure nodes, congestion and void region, but also yield higher ratio of successful transmission, smaller transmission delay and reduced number of control packets.

Evaluation of Real-Time and Off-Line Performance of the Virtual Seismologist Earthquake Early Warning Algorithm in Switzerland

NASA Astrophysics Data System (ADS)

Behr, Yannik; Clinton, John; Cua, Georgia; Cauzzi, Carlo; Heimers, Stefan; Kästli, Philipp; Becker, Jan; Heaton, Thomas

2013-04-01

The Virtual Seismologist (VS) method is a Bayesian approach to regional network-based earthquake early warning (EEW) originally formulated by Cua and Heaton (2007). Implementation of VS into real-time EEW codes has been an on-going effort of the Swiss Seismological Service at ETH Zürich since 2006, with support from ETH Zürich, various European projects, and the United States Geological Survey (USGS). VS is one of three EEW algorithms that form the basis of the California Integrated Seismic Network (CISN) ShakeAlert system, a USGS-funded prototype end-to-end EEW system that could potentially be implemented in California. In Europe, VS is currently operating as a real-time test system in Switzerland. As part of the on-going EU project REAKT (Strategies and Tools for Real-Time Earthquake Risk Reduction), VS installations in southern Italy, western Greece, Istanbul, Romania, and Iceland are planned or underway. In Switzerland, VS has been running in real-time on stations monitored by the Swiss Seismological Service (including stations from Austria, France, Germany, and Italy) since 2010. While originally based on the Earthworm system it has recently been ported to the SeisComp3 system. Besides taking advantage of SeisComp3's picking and phase association capabilities it greatly simplifies the potential installation of VS at networks in particular those already running SeisComp3. We present the architecture of the new SeisComp3 based version and compare its results from off-line tests with the real-time performance of VS in Switzerland over the past two years. We further show that the empirical relationships used by VS to estimate magnitudes and ground motion, originally derived from southern California data, perform well in Switzerland.

Limits to high-speed simulations of spiking neural networks using general-purpose computers.

PubMed

Zenke, Friedemann; Gerstner, Wulfram

2014-01-01

To understand how the central nervous system performs computations using recurrent neuronal circuitry, simulations have become an indispensable tool for theoretical neuroscience. To study neuronal circuits and their ability to self-organize, increasing attention has been directed toward synaptic plasticity. In particular spike-timing-dependent plasticity (STDP) creates specific demands for simulations of spiking neural networks. On the one hand a high temporal resolution is required to capture the millisecond timescale of typical STDP windows. On the other hand network simulations have to evolve over hours up to days, to capture the timescale of long-term plasticity. To do this efficiently, fast simulation speed is the crucial ingredient rather than large neuron numbers. Using different medium-sized network models consisting of several thousands of neurons and off-the-shelf hardware, we compare the simulation speed of the simulators: Brian, NEST and Neuron as well as our own simulator Auryn. Our results show that real-time simulations of different plastic network models are possible in parallel simulations in which numerical precision is not a primary concern. Even so, the speed-up margin of parallelism is limited and boosting simulation speeds beyond one tenth of real-time is difficult. By profiling simulation code we show that the run times of typical plastic network simulations encounter a hard boundary. This limit is partly due to latencies in the inter-process communications and thus cannot be overcome by increased parallelism. Overall, these results show that to study plasticity in medium-sized spiking neural networks, adequate simulation tools are readily available which run efficiently on small clusters. However, to run simulations substantially faster than real-time, special hardware is a prerequisite.

Point Positioning Service for Natural Hazard Monitoring

NASA Astrophysics Data System (ADS)

Bar-Sever, Y. E.

2014-12-01

In an effort to improve natural hazard monitoring, JPL has invested in updating and enlarging its global real-time GNSS tracking network, and has launched a unique service - real-time precise positioning for natural hazard monitoring, entitled GREAT Alert (GNSS Real-Time Earthquake and Tsunami Alert). GREAT Alert leverages the full technological and operational capability of the JPL's Global Differential GPS System [www.gdgps.net] to offer owners of real-time dual-frequency GNSS receivers: Sub-5 cm (3D RMS) real-time, absolute positioning in ITRF08, regardless of location Under 5 seconds turnaround time Full covariance information Estimates of ancillary parameters (such as troposphere) optionally provided This service enables GNSS networks operators to instantly have access to the most accurate and reliable real-time positioning solutions for their sites, and also to the hundreds of participating sites globally, assuring inter-consistency and uniformity across all solutions. Local authorities with limited technical and financial resources can now access to the best technology, and share environmental data to the benefit of the entire pacific region. We will describe the specialized precise point positioning techniques employed by the GREAT Alert service optimized for natural hazard monitoring, and in particular Earthquake monitoring. We address three fundamental aspects of these applications: 1) small and infrequent motion, 2) the availability of data at a central location, and 3) the need for refined solutions at several time scales

A real-time and closed-loop control algorithm for cascaded multilevel inverter based on artificial neural network.

PubMed

Wang, Libing; Mao, Chengxiong; Wang, Dan; Lu, Jiming; Zhang, Junfeng; Chen, Xun

2014-01-01

In order to control the cascaded H-bridges (CHB) converter with staircase modulation strategy in a real-time manner, a real-time and closed-loop control algorithm based on artificial neural network (ANN) for three-phase CHB converter is proposed in this paper. It costs little computation time and memory. It has two steps. In the first step, hierarchical particle swarm optimizer with time-varying acceleration coefficient (HPSO-TVAC) algorithm is employed to minimize the total harmonic distortion (THD) and generate the optimal switching angles offline. In the second step, part of optimal switching angles are used to train an ANN and the well-designed ANN can generate optimal switching angles in a real-time manner. Compared with previous real-time algorithm, the proposed algorithm is suitable for a wider range of modulation index and results in a smaller THD and a lower calculation time. Furthermore, the well-designed ANN is embedded into a closed-loop control algorithm for CHB converter with variable direct voltage (DC) sources. Simulation results demonstrate that the proposed closed-loop control algorithm is able to quickly stabilize load voltage and minimize the line current's THD (<5%) when subjecting the DC sources disturbance or load disturbance. In real design stage, a switching angle pulse generation scheme is proposed and experiment results verify its correctness.

Development of a Network RTK Positioning and Gravity-Surveying Application with Gravity Correction Using a Smartphone

PubMed Central

Kim, Jinsoo; Lee, Youngcheol; Cha, Sungyeoul; Choi, Chuluong; Lee, Seongkyu

2013-01-01

This paper proposes a smartphone-based network real-time kinematic (RTK) positioning and gravity-surveying application (app) that allows semi-real-time measurements using the built-in Bluetooth features of the smartphone and a third-generation or long-term evolution wireless device. The app was implemented on a single smartphone by integrating a global navigation satellite system (GNSS) controller, a laptop, and a field-note writing tool. The observation devices (i.e., a GNSS receiver and relative gravimeter) functioned independently of this system. The app included a gravity module, which converted the measured relative gravity reading into an absolute gravity value according to tides; meter height; instrument drift correction; and network adjustments. The semi-real-time features of this app allowed data to be shared easily with other researchers. Moreover, the proposed smartphone-based gravity-survey app was easily adaptable to various locations and rough terrain due to its compact size. PMID:23857258

A Hyper-Dense GNSS Receiver Network for Monitoring Time and Spatial Variations of Precipitable Water Vapor (PWV)

NASA Astrophysics Data System (ADS)

Tsuda, T.; Ito, N.; Takeda, Y.; Realini, E.; Shinbori, A.

2016-12-01

We employ the GNSS meteorology technique to measure precipitable water vapor (PWV) from the propagation delay of GNSS signal in the atmosphere. We installed a hyper-dense GNSS network using 15 receivers with a horizontal spacing of 1-2 km in Uji, Japan (Uji network). We also obtained precipitation with a rain gauge at a nearby operational weather station and rain cloud distribution by an X-band radar. We selected 40 days from April 2011 to March 2013, when considerable precipitation was detected. Difference in PWV within 10 km was 3-10 mm during a heavy rain. We found PWV increased 10-20 minutes before a passage of a rain cloud. The maximum value of PWV correlated well with the amount of precipitation on the ground. The variance of PWV between the GNSS sites was enhanced during a heavy rain. For a future practical hyper-dense GNSS network system with many receivers, we consider to use inexpensive single frequency (SF) receivers. Because SF receiver cannot eliminate the ionospheric delay by itself, we interpolate the delay referring the delay measured by the nearby dual frequency (DF) receivers. We investigated ionospheric delay by the Uji network, taking advantages of Quasi-Zenith Satellite System (QZSS) that gives signals at high elevation angles. During a travelling ionospheric disturbance (TID), a wavy structure with a horizontal scale of several tens km was recognized. The ionospheric delay was compensated by a linear and quadratic interpolation, then the resulting error of PWV compared with DF solution was about 1.50 mm in RMS. For a real-time estimation of PWV, we used real-time satellite clock information corrected by GEONET. Difference of PWV between the real-time analysis and the post processing with the final orbit was 0.7 mm in RMS. We estimated an overall error of PWV with a dense SF-receiver network on a real-time basis was 1.7 mm in RMS.

Reliable Wireless Broadcast with Linear Network Coding for Multipoint-to-Multipoint Real-Time Communications

NASA Astrophysics Data System (ADS)

Kondo, Yoshihisa; Yomo, Hiroyuki; Yamaguchi, Shinji; Davis, Peter; Miura, Ryu; Obana, Sadao; Sampei, Seiichi

This paper proposes multipoint-to-multipoint (MPtoMP) real-time broadcast transmission using network coding for ad-hoc networks like video game networks. We aim to achieve highly reliable MPtoMP broadcasting using IEEE 802.11 media access control (MAC) that does not include a retransmission mechanism. When each node detects packets from the other nodes in a sequence, the correctly detected packets are network-encoded, and the encoded packet is broadcasted in the next sequence as a piggy-back for its native packet. To prevent increase of overhead in each packet due to piggy-back packet transmission, network coding vector for each node is exchanged between all nodes in the negotiation phase. Each user keeps using the same coding vector generated in the negotiation phase, and only coding information that represents which user signal is included in the network coding process is transmitted along with the piggy-back packet. Our simulation results show that the proposed method can provide higher reliability than other schemes using multi point relay (MPR) or redundant transmissions such as forward error correction (FEC). We also implement the proposed method in a wireless testbed, and show that the proposed method achieves high reliability in a real-world environment with a practical degree of complexity when installed on current wireless devices.

Socioscape: Real-Time Analysis of Dynamic Heterogeneous Networks In Complex Socio-Cultural Systems

DTIC Science & Technology

2015-10-22

Cluster Mixed-Membership Blockmodel for Time-Evolving Networks, Proceedings of the 14th International Conference on Artifical Intelligence and...Learning With Simultaneous Orthogonal Matching Pursuit, Proceedings of the 13th International Conference on Artifical Intelligence and Statistics

Predicting local field potentials with recurrent neural networks.

PubMed

Kim, Louis; Harer, Jacob; Rangamani, Akshay; Moran, James; Parks, Philip D; Widge, Alik; Eskandar, Emad; Dougherty, Darin; Chin, Sang Peter

2016-08-01

We present a Recurrent Neural Network using LSTM (Long Short Term Memory) that is capable of modeling and predicting Local Field Potentials. We train and test the network on real data recorded from epilepsy patients. We construct networks that predict multi-channel LFPs for 1, 10, and 100 milliseconds forward in time. Our results show that prediction using LSTM outperforms regression when predicting 10 and 100 millisecond forward in time.

Use of high performance networks and supercomputers for real-time flight simulation

NASA Technical Reports Server (NTRS)

Cleveland, Jeff I., II

1993-01-01

In order to meet the stringent time-critical requirements for real-time man-in-the-loop flight simulation, computer processing operations must be consistent in processing time and be completed in as short a time as possible. These operations include simulation mathematical model computation and data input/output to the simulators. In 1986, in response to increased demands for flight simulation performance, NASA's Langley Research Center (LaRC), working with the contractor, developed extensions to the Computer Automated Measurement and Control (CAMAC) technology which resulted in a factor of ten increase in the effective bandwidth and reduced latency of modules necessary for simulator communication. This technology extension is being used by more than 80 leading technological developers in the United States, Canada, and Europe. Included among the commercial applications are nuclear process control, power grid analysis, process monitoring, real-time simulation, and radar data acquisition. Personnel at LaRC are completing the development of the use of supercomputers for mathematical model computation to support real-time flight simulation. This includes the development of a real-time operating system and development of specialized software and hardware for the simulator network. This paper describes the data acquisition technology and the development of supercomputing for flight simulation.

Change and Anomaly Detection in Real-Time GPS Data

NASA Astrophysics Data System (ADS)

Granat, R.; Pierce, M.; Gao, X.; Bock, Y.

2008-12-01

The California Real-Time Network (CRTN) is currently generating real-time GPS position data at a rate of 1-2Hz at over 80 locations. The CRTN data presents the possibility of studying dynamical solid earth processes in a way that complements existing seismic networks. To realize this possibility we have developed a prototype system for detecting changes and anomalies in the real-time data. Through this system, we can can correlate changes in multiple stations in order to detect signals with geographical extent. Our approach involves developing a statistical model for each GPS station in the network, and then using those models to segment the time series into a number of discrete states described by the model. We use a hidden Markov model (HMM) to describe the behavior of each station; fitting the model to the data requires neither labeled training examples nor a priori information about the system. As such, HMMs are well suited to this problem domain, in which the data remains largely uncharacterized. There are two main components to our approach. The first is the model fitting algorithm, regularized deterministic annealing expectation- maximization (RDAEM), which provides robust, high-quality results. The second is a web service infrastructure that connects the data to the statistical modeling analysis and allows us to easily present the results of that analysis through a web portal interface. This web service approach facilitates the automatic updating of station models to keep pace with dynamical changes in the data. Our web portal interface is critical to the process of interpreting the data. A Google Maps interface allows users to visually interpret state changes not only on individual stations but across the entire network. Users can drill down from the map interface to inspect detailed results for individual stations, download the time series data, and inspect fitted models. Alternatively, users can use the web portal look at the evolution of changes on the network by moving backwards and forwards in time.

Integration of Infrasound, Atmospheric Pressure, and Seismic Observations with the NSF EarthScope USArray Transportable Array

NASA Astrophysics Data System (ADS)

Vernon, F.; Tytell, J.; Hedlin, M. A. H.; Walker, K.; Busby, R.; Woodward, R.

2012-04-01

Earthscope's USArray Transportable Array (TA) network serves as a real-time monitoring and recording platform for both seismic and weather phenomena. To date, most of the approximately 500 TA stations have been retrofitted with VTI SCP1000 MEMS barometric pressure gauges capable of recording data at 1 sample per second (sps). Additionally, over 300 of the TA stations have also been retrofitted with Setra 278 barometric gauges and NCPA infrasound sensors capable of recording data at 1 and 40 sps. While individual seismic events have been successfully researched via the TA network, observations of powerful weather events by the TA network have yet to be embraced by the scientific community. This presentation will focus on case studies involving severe weather passage across portions of the TA network throughout 2011 in order to highlight its viability as a platform for real-time weather monitoring and research. It will also highlight the coupling of atmospheric signals into the seismic observations. Examples of gust front passages and pressure couplets from severe thunderstorms will be presented, as will observations of multiple tornados occurred in the Spring of 2011. These data will demonstrate the overall viability of the TA network for monitoring severe weather events in real-time.

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

PubMed

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

2011-01-01

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

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

PubMed Central

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

2011-01-01

Fire hazard monitoring and evacuation for building environments is a novel application area for the deployment of wireless sensor networks. In this context, adaptive routing is essential in order to ensure safe and timely data delivery in building evacuation and fire fighting resource applications. Existing routing mechanisms for wireless sensor networks are not well suited for building fires, especially as they do not consider critical and dynamic network scenarios. In this paper, an emergency-adaptive, real-time and robust routing protocol is presented for emergency situations such as building fire hazard applications. The protocol adapts to handle dynamic emergency scenarios and works well with the routing hole problem. Theoretical analysis and simulation results indicate that our protocol provides a real-time routing mechanism that is well suited for dynamic emergency scenarios in building fires when compared with other related work. PMID:22163774

BENEFITS OF SEWERAGE SYSTEM REAL-TIME CONTROL

EPA Science Inventory

Real-time control (RTC) is a custom-designed computer-assisted management system for a specific urban sewerage network that is activated during a wet-weather flow event. Though uses of RTC systems had started in the mid 60s, recent developments in computers, telecommunication, in...

REAL-TIME CONTROL OF COMBINED SEWER NETWORKS

EPA Science Inventory

Real-time control (RTC) is a custom-designed management program for a specific urban sewerage system during a wet-weather event. The function of RTC is to assure efficient operation of the sewerage system and maximum utilization of existing storage capacity, either to fully conta...

A ZigBee wireless networking for remote sensing applications in hydrological monitoring system

NASA Astrophysics Data System (ADS)

Weng, Songgan; Zhai, Duo; Yang, Xing; Hu, Xiaodong

2017-01-01

Hydrological monitoring is recognized as one of the most important factors in hydrology. Particularly, investigation of the tempo-spatial variation patterns of water-level and their effect on hydrological research has attracted more and more attention in recent. Because of the limitations in both human costs and existing water-level monitoring devices, however, it is very hard for researchers to collect real-time water-level data from large-scale geographical areas. This paper designs and implements a real-time water-level data monitoring system (MCH) based on ZigBee networking, which explicitly serves as an effective and efficient scientific instrument for domain experts to facilitate the measurement of large-scale and real-time water-level data monitoring. We implement a proof-of-concept prototype of the MCH, which can monitor water-level automatically, real-timely and accurately with low cost and low power consumption. The preliminary laboratory results and analyses demonstrate the feasibility and the efficacy of the MCH.

Radio astronomy interferometer network testing for a Malaysia-China real-time e-VLBI

NASA Astrophysics Data System (ADS)

Abidin, Zamri Zainal; Hashim, Shaiful Jahari; Wei, Lim Yang; Zhong, Chen; Rosli, Zulfazli

2018-01-01

The uv-coverage of the current VLBI network between Australia northern Asia will be significantly enhanced with an existence of a middle baseline VLBI station located in Malaysia. This paper investigated the connecting route of the first half of the Asia-Oceania VLBI network i.e. from Malaysia to China. The investigation of transmission network characteristics between Malaysia and China was carried out in order to perform a real-time and reliable data transfer within the e-VLBI network for future eVLBI observations. MyREN (Malaysia) and CSTNET (China) high-speed research networks were utilized for this proposed e-VLBI connection. Preliminary network test was performed by ping, traceroute, and iperf prior to data transfer tests, which were evaluated with three types of protocols namely FTP, Tsunami-UDT and UDT. The results showed that, on average, there were eighteen hops between Malaysia and China networks with 98 ms round trip time (RTT) delay. Overall UDP protocol has a better throughput compared to TCP protocol. UDP can reach a maximum rate of 90 Mbps with 0% packet loss. In this feasibility test, the VLBI test data was successfully transferred between Malaysia and China by utilizing the three types of data transfer protocols.

Reorganization of functional brain networks mediates the improvement of cognitive performance following real-time neurofeedback training of working memory.

PubMed

Zhang, Gaoyan; Yao, Li; Shen, Jiahui; Yang, Yihong; Zhao, Xiaojie

2015-05-01

Working memory (WM) is essential for individuals' cognitive functions. Neuroimaging studies indicated that WM fundamentally relied on a frontoparietal working memory network (WMN) and a cinguloparietal default mode network (DMN). Behavioral training studies demonstrated that the two networks can be modulated by WM training. Different from the behavioral training, our recent study used a real-time functional MRI (rtfMRI)-based neurofeedback method to conduct WM training, demonstrating that WM performance can be significantly improved after successfully upregulating the activity of the target region of interest (ROI) in the left dorsolateral prefrontal cortex (Zhang et al., [2013]: PloS One 8:e73735); however, the neural substrate of rtfMRI-based WM training remains unclear. In this work, we assessed the intranetwork and internetwork connectivity changes of WMN and DMN during the training, and their correlations with the change of brain activity in the target ROI as well as with the improvement of post-training behavior. Our analysis revealed an "ROI-network-behavior" correlation relationship underlying the rtfMRI training. Further mediation analysis indicated that the reorganization of functional brain networks mediated the effect of self-regulation of the target brain activity on the improvement of cognitive performance following the neurofeedback training. The results of this study enhance our understanding of the neural basis of real-time neurofeedback and suggest a new direction to improve WM performance by regulating the functional connectivity in the WM related networks. © 2014 Wiley Periodicals, Inc.

Nonuniform traffic spots (NUTS) in multistage interconnection networks

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

Lang, T.; Kurisaki, L.

1990-09-01

The performance of multistage interconnection networks for multiprocessors is degraded when the traffic pattern produces nonuniform congestion in the blocking switches, that is, when there exist nonuniform traffic spots. For some specific patterns the authors evaluate this degradation in performance and propose modifications to the network organization and operation to reduce the degradation. Successful modifications are the use of diverting switches and the extension of the network with additional links. The use of these modifications makes the network more effective for a larger variety of traffic patterns. The authors also consider the case in which the network carries the superpositionmore » of two types of traffic. One type is the high throughput data and instruction traffic, while the other consists of control and I/O packets which are of low throughput but have severe real-time constraints. The authors conclude that diverting switches and networks with additional links are also suitable for assuring low latency for the real-time traffic, especially when using the displacing mode.« less

Georgia's Stream-Water-Quality Monitoring Network, 2006

USGS Publications Warehouse

Nobles, Patricia L.; ,

2006-01-01

The USGS stream-water-quality monitoring network for Georgia is an aggregation of smaller networks and individual monitoring stations that have been established in cooperation with Federal, State, and local agencies. These networks collectively provide data from 130 sites, 62 of which are monitored continuously in real time using specialized equipment that transmits these data via satellite to a centralized location for processing and storage. These data are made available on the Web in near real time at http://waterdata.usgs.gov/ga/nwis/ Ninety-eight stations are sampled periodically for a more extensive suite of chemical and biological constituents that require laboratory analysis. Both the continuous and the periodic water-quality data are archived and maintained in the USGS National Water Information System and are available to cooperators, water-resource managers, and the public. The map at right shows the USGS stream-water-quality monitoring network for Georgia and major watersheds. The network represents an aggregation of smaller networks and individual monitoring stations that collectively provide data from 130 sites.

The Real Time Mission Monitor: A Situational Awareness Tool For Managing Experiment Assets

NASA Technical Reports Server (NTRS)

Blakeslee, Richard; Hall, John; Goodman, Michael; Parker, Philip; Freudinger, Larry; He, Matt

2007-01-01

The NASA Real Time Mission Monitor (RTMM) is a situational awareness tool that integrates satellite, airborne and surface data sets; weather information; model and forecast outputs; and vehicle state data (e.g., aircraft navigation, satellite tracks and instrument field-of-views) for field experiment management RTMM optimizes science and logistic decision-making during field experiments by presenting timely data and graphics to the users to improve real time situational awareness of the experiment's assets. The RTMM is proven in the field as it supported program managers, scientists, and aircraft personnel during the NASA African Monsoon Multidisciplinary Analyses experiment during summer 2006 in Cape Verde, Africa. The integration and delivery of this information is made possible through data acquisition systems, network communication links and network server resources built and managed by collaborators at NASA Dryden Flight Research Center (DFRC) and Marshall Space Flight Center (MSFC). RTMM is evolving towards a more flexible and dynamic combination of sensor ingest, network computing, and decision-making activities through the use of a service oriented architecture based on community standards and protocols.

SafeTrip 21 initiative : networked traveler foresighted driving field experiment, final report.

DOT National Transportation Integrated Search

2011-04-01

The Networked Traveler Project was originally conceived to leverage the explosive rise of smartphones as a : communications gateway to bring real-time traveler assistance concepts from the ITS community to the American : people. The Networked Travele...

End-User Applications of Real-Time Earthquake Information in Europe

NASA Astrophysics Data System (ADS)

Cua, G. B.; Gasparini, P.; Giardini, D.; Zschau, J.; Filangieri, A. R.; Reakt Wp7 Team

2011-12-01

The primary objective of European FP7 project REAKT (Strategies and Tools for Real-Time Earthquake Risk Reduction) is to improve the efficiency of real-time earthquake risk mitigation methods and their capability of protecting structures, infrastructures, and populations. REAKT aims to address the issues of real-time earthquake hazard and response from end-to-end, with efforts directed along the full spectrum of methodology development in earthquake forecasting, earthquake early warning, and real-time vulnerability systems, through optimal decision-making, and engagement and cooperation of scientists and end users for the establishment of best practices for use of real-time information. Twelve strategic test cases/end users throughout Europe have been selected. This diverse group of applications/end users includes civil protection authorities, railway systems, hospitals, schools, industrial complexes, nuclear plants, lifeline systems, national seismic networks, and critical structures. The scale of target applications covers a wide range, from two school complexes in Naples, to individual critical structures, such as the Rion Antirion bridge in Patras, and the Fatih Sultan Mehmet bridge in Istanbul, to large complexes, such as the SINES industrial complex in Portugal and the Thessaloniki port area, to distributed lifeline and transportation networks and nuclear plants. Some end-users are interested in in-depth feasibility studies for use of real-time information and development of rapid response plans, while others intend to install real-time instrumentation and develop customized automated control systems. From the onset, REAKT scientists and end-users will work together on concept development and initial implementation efforts using the data products and decision-making methodologies developed with the goal of improving end-user risk mitigation. The aim of this scientific/end-user partnership is to ensure that scientific efforts are applicable to operational, real-world problems.

Real Time Conference 2014 Overview

NASA Astrophysics Data System (ADS)

Nomachi, Masaharu

2015-06-01

This article presents an overview of the 19th Real Time Conference held last May 26-30, 2014, at the Nara Prefectural New Public Hall, Nara, Japan, organized by the Research Center for Nuclear Physics of the Osaka University. The program included many invited talks and oral sessions offering an extensive overview on the following topics: real-time system architectures, intelligent signal processing, fast data transfer links and networks, trigger systems, data acquisition, processing-farms, control, monitoring and test systems, emerging real-time technologies, new standards, real-time safety and security, and some feedback on experiences. In parallel to the oral and poster presentations, industrial exhibits by companies, workshops and short courses also ran through the week.

Two Phase Admission Control for QoS Mobile Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Chen, Chien-Sheng; Su, Yi-Wen; Liu, Wen-Hsiung; Chi, Ching-Lung

In this paper a novel and effective two phase admission control (TPAC) for QoS mobile ad hoc networks is proposed that satisfies the real-time traffic requirements in mobile ad hoc networks. With a limited amount of extra overhead, TPAC can avoid network congestions by a simple and precise admission control which blocks most of the overloading flow-requests in the route discovery process. When compared with previous QoS routing schemes such as QoS-aware routing protocol and CACP protocols, it is shown from system simulations that the proposed scheme can increase the system throughput and reduce both the dropping rate and the end-to-end delay. Therefore, TPAC is surely an effective QoS-guarantee protocol to provide for real-time traffic.

Design control system of telescope force actuators based on WLAN

NASA Astrophysics Data System (ADS)

Shuai, Xiaoying; Zhang, Zhenchao

2010-05-01

With the development of the technology of autocontrol, telescope, computer, network and communication, the control system of the modern large and extra lager telescope become more and more complicated, especially application of active optics. Large telescope based on active optics maybe contain enormous force actuators. This is a challenge to traditional control system based on wired networks, which result in difficult-to-manage, occupy signification space and lack of system flexibility. Wireless network can resolve these disadvantages of wired network. Presented control system of telescope force actuators based on WLAN (WFCS), designed the control system framework of WFCS. To improve the performance of real-time, we developed software of force actuators control system in Linux. Finally, this paper discussed improvement of WFCS real-time, conceived maybe improvement in the future.

Connectivity-based neurofeedback: Dynamic causal modeling for real-time fMRI☆

PubMed Central

Koush, Yury; Rosa, Maria Joao; Robineau, Fabien; Heinen, Klaartje; W. Rieger, Sebastian; Weiskopf, Nikolaus; Vuilleumier, Patrik; Van De Ville, Dimitri; Scharnowski, Frank

2013-01-01

Neurofeedback based on real-time fMRI is an emerging technique that can be used to train voluntary control of brain activity. Such brain training has been shown to lead to behavioral effects that are specific to the functional role of the targeted brain area. However, real-time fMRI-based neurofeedback so far was limited to mainly training localized brain activity within a region of interest. Here, we overcome this limitation by presenting near real-time dynamic causal modeling in order to provide feedback information based on connectivity between brain areas rather than activity within a single brain area. Using a visual–spatial attention paradigm, we show that participants can voluntarily control a feedback signal that is based on the Bayesian model comparison between two predefined model alternatives, i.e. the connectivity between left visual cortex and left parietal cortex vs. the connectivity between right visual cortex and right parietal cortex. Our new approach thus allows for training voluntary control over specific functional brain networks. Because most mental functions and most neurological disorders are associated with network activity rather than with activity in a single brain region, this novel approach is an important methodological innovation in order to more directly target functionally relevant brain networks. PMID:23668967

vMon-mobile provides wireless connection to the electronic patient record

NASA Astrophysics Data System (ADS)

Oliveira, Pedro P., Jr.; Rebelo, Marina; Pilon, Paulo E.; Gutierrez, Marco A.; Tachinardi, Umberto

2002-05-01

This work presents the development of a set of tools to help doctors to continuously monitor critical patients. Real-time monitoring signals are displayed via a Web Based Electronic Patient Record (Web-EPR) developed at the Heart Institute. Any computer on the Hospital's Intranet can access the Web-EPR that will open a browser plug-in called vMon. Recently vMon was adapted to wireless mobile devices providing the same real-time visualization of vital signals of its desktop counterpart. The monitoring network communicates with the hospital network through a gateway using HL7 messages and has the ability to export waveforms in real time using the multicast protocol through an API library. A dedicated ActiveX component was built that establishes the streaming of the biomedical signals under monitoring and displays them on an Internet Explorer 5.x browser. The mobile version - called vMon-mobile - will parse the browser window and deliver it to a PDA device connected to a local area network. The result is a virtual monitor presenting real-time data on a mobile device. All parameters and signals acquired from the moment the patient is connected to the monitors are stored for a few days. The most clinically relevant information is added to patient's EPR.

A DSP-based neural network non-uniformity correction algorithm for IRFPA

NASA Astrophysics Data System (ADS)

Liu, Chong-liang; Jin, Wei-qi; Cao, Yang; Liu, Xiu

2009-07-01

An effective neural network non-uniformity correction (NUC) algorithm based on DSP is proposed in this paper. The non-uniform response in infrared focal plane array (IRFPA) detectors produces corrupted images with a fixed-pattern noise(FPN).We introduced and analyzed the artificial neural network scene-based non-uniformity correction (SBNUC) algorithm. A design of DSP-based NUC development platform for IRFPA is described. The DSP hardware platform designed is of low power consumption, with 32-bit fixed point DSP TMS320DM643 as the kernel processor. The dependability and expansibility of the software have been improved by DSP/BIOS real-time operating system and Reference Framework 5. In order to realize real-time performance, the calibration parameters update is set at a lower task priority then video input and output in DSP/BIOS. In this way, calibration parameters updating will not affect video streams. The work flow of the system and the strategy of real-time realization are introduced. Experiments on real infrared imaging sequences demonstrate that this algorithm requires only a few frames to obtain high quality corrections. It is computationally efficient and suitable for all kinds of non-uniformity.

Development of real-time voltage stability monitoring tool for power system transmission network using Synchrophasor data

NASA Astrophysics Data System (ADS)

Pulok, Md Kamrul Hasan

Intelligent and effective monitoring of power system stability in control centers is one of the key issues in smart grid technology to prevent unwanted power system blackouts. Voltage stability analysis is one of the most important requirements for control center operation in smart grid era. With the advent of Phasor Measurement Unit (PMU) or Synchrophasor technology, real time monitoring of voltage stability of power system is now a reality. This work utilizes real-time PMU data to derive a voltage stability index to monitor the voltage stability related contingency situation in power systems. The developed tool uses PMU data to calculate voltage stability index that indicates relative closeness of the instability by producing numerical indices. The IEEE 39 bus, New England power system was modeled and run on a Real-time Digital Simulator that stream PMU data over the Internet using IEEE C37.118 protocol. A Phasor data concentrator (PDC) is setup that receives streaming PMU data and stores them in Microsoft SQL database server. Then the developed voltage stability monitoring (VSM) tool retrieves phasor measurement data from SQL server, performs real-time state estimation of the whole network, calculate voltage stability index, perform real-time ranking of most vulnerable transmission lines, and finally shows all the results in a graphical user interface. All these actions are done in near real-time. Control centers can easily monitor the systems condition by using this tool and can take precautionary actions if needed.

Optimizing the real-time automatic location of the events produced in Romania using an advanced processing system

NASA Astrophysics Data System (ADS)

Neagoe, Cristian; Grecu, Bogdan; Manea, Liviu

2016-04-01

National Institute for Earth Physics (NIEP) operates a real time seismic network which is designed to monitor the seismic activity on the Romanian territory, which is dominated by the intermediate earthquakes (60-200 km) from Vrancea area. The ability to reduce the impact of earthquakes on society depends on the existence of a large number of high-quality observational data. The development of the network in recent years and an advanced seismic acquisition are crucial to achieving this objective. The software package used to perform the automatic real-time locations is Seiscomp3. An accurate choice of the Seiscomp3 setting parameters is necessary to ensure the best performance of the real-time system i.e., the most accurate location for the earthquakes and avoiding any false events. The aim of this study is to optimize the algorithms of the real-time system that detect and locate the earthquakes in the monitored area. This goal is pursued by testing different parameters (e.g., STA/LTA, filters applied to the waveforms) on a data set of representative earthquakes of the local seismicity. The results are compared with the locations from the Romanian Catalogue ROMPLUS.

Small-time Scale Network Traffic Prediction Based on Complex-valued Neural Network

NASA Astrophysics Data System (ADS)

Yang, Bin

2017-07-01

Accurate models play an important role in capturing the significant characteristics of the network traffic, analyzing the network dynamic, and improving the forecasting accuracy for system dynamics. In this study, complex-valued neural network (CVNN) model is proposed to further improve the accuracy of small-time scale network traffic forecasting. Artificial bee colony (ABC) algorithm is proposed to optimize the complex-valued and real-valued parameters of CVNN model. Small-scale traffic measurements data namely the TCP traffic data is used to test the performance of CVNN model. Experimental results reveal that CVNN model forecasts the small-time scale network traffic measurement data very accurately

The Future of Operational Space Weather Observations

NASA Astrophysics Data System (ADS)

Berger, T. E.

2015-12-01

We review the current state of operational space weather observations, the requirements for new or evolved space weather forecasting capablities, and the relevant sections of the new National strategy for space weather developed by the Space Weather Operations, Research, and Mitigation (SWORM) Task Force chartered by the Office of Science and Technology Policy of the White House. Based on this foundation, we discuss future space missions such as the NOAA space weather mission to the L1 Lagrangian point planned for the 2021 time frame and its synergy with an L5 mission planned for the same period; the space weather capabilities of the upcoming GOES-R mission, as well as GOES-Next possiblities; and the upcoming COSMIC-2 mission for ionospheric observations. We also discuss the needs for ground-based operational networks to supply mission critical and/or backup space weather observations including the NSF GONG solar optical observing network, the USAF SEON solar radio observing network, the USGS real-time magnetometer network, the USCG CORS network of GPS receivers, and the possibility of operationalizing the world-wide network of neutron monitors for real-time alerts of ground-level radiation events.

Value of Information for Optimal Adaptive Routing in Stochastic Time-Dependent Traffic Networks: Algorithms and Computational Tools

DOT National Transportation Integrated Search

2010-10-25

Real-time information is important for travelers' routing decisions in uncertain networks by enabling online adaptation to revealed traffic conditions. Usually there are spatial and/or temporal limitations in traveler information. In this research, a...

In-network adaptation of SHVC video in software-defined networks

NASA Astrophysics Data System (ADS)

Awobuluyi, Olatunde; Nightingale, James; Wang, Qi; Alcaraz Calero, Jose Maria; Grecos, Christos

2016-04-01

Software Defined Networks (SDN), when combined with Network Function Virtualization (NFV) represents a paradigm shift in how future networks will behave and be managed. SDN's are expected to provide the underpinning technologies for future innovations such as 5G mobile networks and the Internet of Everything. The SDN architecture offers features that facilitate an abstracted and centralized global network view in which packet forwarding or dropping decisions are based on application flows. Software Defined Networks facilitate a wide range of network management tasks, including the adaptation of real-time video streams as they traverse the network. SHVC, the scalable extension to the recent H.265 standard is a new video encoding standard that supports ultra-high definition video streams with spatial resolutions of up to 7680×4320 and frame rates of 60fps or more. The massive increase in bandwidth required to deliver these U-HD video streams dwarfs the bandwidth requirements of current high definition (HD) video. Such large bandwidth increases pose very significant challenges for network operators. In this paper we go substantially beyond the limited number of existing implementations and proposals for video streaming in SDN's all of which have primarily focused on traffic engineering solutions such as load balancing. By implementing and empirically evaluating an SDN enabled Media Adaptation Network Entity (MANE) we provide a valuable empirical insight into the benefits and limitations of SDN enabled video adaptation for real time video applications. The SDN-MANE is the video adaptation component of our Video Quality Assurance Manager (VQAM) SDN control plane application, which also includes an SDN monitoring component to acquire network metrics and a decision making engine using algorithms to determine the optimum adaptation strategy for any real time video application flow given the current network conditions. Our proposed VQAM application has been implemented and evaluated on an SDN allowing us to provide important benchmarks for video streaming over SDN and for SDN control plane latency.

The signal extraction of fetal heart rate based on wavelet transform and BP neural network

NASA Astrophysics Data System (ADS)

Yang, Xiao Hong; Zhang, Bang-Cheng; Fu, Hu Dai

2005-04-01

This paper briefly introduces the collection and recognition of bio-medical signals, designs the method to collect FM signals. A detailed discussion on the system hardware, structure and functions is also given. Under LabWindows/CVI,the hardware and the driver do compatible, the hardware equipment work properly actively. The paper adopts multi threading technology for real-time analysis and makes use of latency time of CPU effectively, expedites program reflect speed, improves the program to perform efficiency. One threading is collecting data; the other threading is analyzing data. Using the method, it is broaden to analyze the signal in real-time. Wavelet transform to remove the main interference in the FM and by adding time-window to recognize with BP network; Finally the results of collecting signals and BP networks are discussed. 8 pregnant women's signals of FM were collected successfully by using the sensor. The correctness rate of BP network recognition is about 83.3% by using the above measure.

Objective assessment of MPEG-2 video quality

NASA Astrophysics Data System (ADS)

Gastaldo, Paolo; Zunino, Rodolfo; Rovetta, Stefano

2002-07-01

The increasing use of video compression standards in broadcasting television systems has required, in recent years, the development of video quality measurements that take into account artifacts specifically caused by digital compression techniques. In this paper we present a methodology for the objective quality assessment of MPEG video streams by using circular back-propagation feedforward neural networks. Mapping neural networks can render nonlinear relationships between objective features and subjective judgments, thus avoiding any simplifying assumption on the complexity of the model. The neural network processes an instantaneous set of input values, and yields an associated estimate of perceived quality. Therefore, the neural-network approach turns objective quality assessment into adaptive modeling of subjective perception. The objective features used for the estimate are chosen according to the assessed relevance to perceived quality and are continuously extracted in real time from compressed video streams. The overall system mimics perception but does not require any analytical model of the underlying physical phenomenon. The capability to process compressed video streams represents an important advantage over existing approaches, like avoiding the stream-decoding process greatly enhances real-time performance. Experimental results confirm that the system provides satisfactory, continuous-time approximations for actual scoring curves concerning real test videos.

Wideband, mobile networking technologies

NASA Astrophysics Data System (ADS)

Hyer, Kevin L.; Bowen, Douglas G.; Pulsipher, Dennis C.

2005-05-01

Ubiquitous communications will be the next era in the evolving communications revolution. From the human perspective, access to information will be instantaneous and provide a revolution in services available to both the consumer and the warfighter. Services will be from the mundane - anytime, anywhere access to any movie ever made - to the vital - reliable and immediate access to the analyzed real-time video from the multi-spectral sensors scanning for snipers in the next block. In the former example, the services rely on a fixed infrastructure of networking devices housed in controlled environments and coupled to fixed terrestrial fiber backbones - in the latter, the services are derived from an agile and highly mobile ad-hoc backbone established in a matter of minutes by size, weight, and power-constrained platforms. This network must mitigate significant changes in the transmission media caused by millisecond-scale atmospheric temperature variations, the deployment of smoke, or the drifting of a cloud. It must mitigate against structural obscurations, jet wash, or incapacitation of a node. To maintain vital connectivity, the mobile backbone must be predictive and self-healing on both near-real-time and real-time time scales. The nodes of this network must be reconfigurable to mitigate intentional and environmental jammers, block attackers, and alleviate interoperability concerns caused by changing standards. The nodes must support multi-access of disparate waveform and protocols.

Metric projection for dynamic multiplex networks.

PubMed

Jurman, Giuseppe

2016-08-01

Evolving multiplex networks are a powerful model for representing the dynamics along time of different phenomena, such as social networks, power grids, biological pathways. However, exploring the structure of the multiplex network time series is still an open problem. Here we propose a two-step strategy to tackle this problem based on the concept of distance (metric) between networks. Given a multiplex graph, first a network of networks is built for each time step, and then a real valued time series is obtained by the sequence of (simple) networks by evaluating the distance from the first element of the series. The effectiveness of this approach in detecting the occurring changes along the original time series is shown on a synthetic example first, and then on the Gulf dataset of political events.

Towards a Low-Cost Remote Memory Attestation for the Smart Grid

PubMed Central

Yang, Xinyu; He, Xiaofei; Yu, Wei; Lin, Jie; Li, Rui; Yang, Qingyu; Song, Houbing

2015-01-01

In the smart grid, measurement devices may be compromised by adversaries, and their operations could be disrupted by attacks. A number of schemes to efficiently and accurately detect these compromised devices remotely have been proposed. Nonetheless, most of the existing schemes detecting compromised devices depend on the incremental response time in the attestation process, which are sensitive to data transmission delay and lead to high computation and network overhead. To address the issue, in this paper, we propose a low-cost remote memory attestation scheme (LRMA), which can efficiently and accurately detect compromised smart meters considering real-time network delay and achieve low computation and network overhead. In LRMA, the impact of real-time network delay on detecting compromised nodes can be eliminated via investigating the time differences reported from relay nodes. Furthermore, the attestation frequency in LRMA is dynamically adjusted with the compromised probability of each node, and then, the total number of attestations could be reduced while low computation and network overhead can be achieved. Through a combination of extensive theoretical analysis and evaluations, our data demonstrate that our proposed scheme can achieve better detection capacity and lower computation and network overhead in comparison to existing schemes. PMID:26307998

Towards a Low-Cost Remote Memory Attestation for the Smart Grid.

PubMed

Yang, Xinyu; He, Xiaofei; Yu, Wei; Lin, Jie; Li, Rui; Yang, Qingyu; Song, Houbing

2015-08-21

In the smart grid, measurement devices may be compromised by adversaries, and their operations could be disrupted by attacks. A number of schemes to efficiently and accurately detect these compromised devices remotely have been proposed. Nonetheless, most of the existing schemes detecting compromised devices depend on the incremental response time in the attestation process, which are sensitive to data transmission delay and lead to high computation and network overhead. To address the issue, in this paper, we propose a low-cost remote memory attestation scheme (LRMA), which can efficiently and accurately detect compromised smart meters considering real-time network delay and achieve low computation and network overhead. In LRMA, the impact of real-time network delay on detecting compromised nodes can be eliminated via investigating the time differences reported from relay nodes. Furthermore, the attestation frequency in LRMA is dynamically adjusted with the compromised probability of each node, and then, the total number of attestations could be reduced while low computation and network overhead can be achieved. Through a combination of extensive theoretical analysis and evaluations, our data demonstrate that our proposed scheme can achieve better detection capacity and lower computation and network overhead in comparison to existing schemes.

Development of a Real-Time GPS/Seismic Displacement Meter: Applications to Civilian Infrastructure in Orange and Western Riverside Counties, California

NASA Technical Reports Server (NTRS)

Bock, Yehuda

2005-01-01

We propose a three-year applications project that will develop an Integrated Real-Time GPS/Seismic System and deploy it in Orange and Western Riverside Counties, spanning three major strike-slip faults in southern California (San Andreas, San Jacinto, and Elsinore) and significant populations and civilian infrastructure. The system relying on existing GPS and seismic networks will collect and analyze GPS and seismic data for the purpose of estimating and disseminating real-time positions and total ground displacements (dynamic, as well as static) during all phases of the seismic cycle, from fractions of seconds to years. Besides its intrinsic scientific use as a real-time displacement meter (transducer), the GPS/Seismic System will be a powerful tool for local and state decision makers for risk mitigation, disaster management, and structural monitoring (dams, bridges, and buildings). Furthermore, the GPS/Seismic System will become an integral part of California's spatial referencing and positioning infrastructure, which is complicated by tectonic motion, seismic displacements, and land subsidence. Finally, the GPS/Seismic system will also be applicable to navigation in any environment (land, sea, or air) by combining precise real-time instantaneous GPS positioning with inertial navigation systems. This development will take place under the umbrella of the California Spatial Reference Center, in partnership with local (Counties, Riverside County Flood and Water Conservation District, Metropolitan Water District), state (Caltrans), and Federal agencies (NGS, NASA, USGS), the geophysics community (SCIGN/SCEC2), and the private sector (RBF Consulting). The project will leverage considerable funding, resources, and R&D from SCIGN, CSRC and two NSF-funded IT projects at UCSD and SDSU: RoadNet (Real-Time Observatories, Applications and Data Management Network) and the High Performance Wireless Research and Education Network (HPWREN). These two projects are funded to develop both the wireless networks and the integrated, seamless, and transparent information management system that will deliver seismic, geodetic, oceanographic, hydrological, ecological, and physical data to a variety of end users in real-time in the San Diego region. CSRC is interested in providing users access to real-time, accurate GPS data for a wide variety of applications including RTK surveying/GIS and positioning of moving platforms such as aircraft and emergency vehicles. SCIGN is interested in upgrading sites to high-frequency real-time operations for rapid earthquake response and GPS seismology. The successful outcome of the project will allow the implementation of similar systems elsewhere, particularly in plate boundary zones with significant populations and civilian infrastructure. CSRC would like to deploy the GPS/Seismic System in other parts of California, in particular San Diego, Los Angeles County and the San Francisco Bay Area.

Development of real time monitor system displaying seismic waveform data observed at seafloor seismic network, DONET, for disaster management information

NASA Astrophysics Data System (ADS)

Horikawa, H.; Takaesu, M.; Sueki, K.; Takahashi, N.; Sonoda, A.; Miura, S.; Tsuboi, S.

2014-12-01

Mega-thrust earthquakes are anticipated to occur in the Nankai Trough in southwest Japan. In the source areas, we have deployed seafloor seismic network, DONET (Dense Ocean-floor Network System for Earthquake and Tsunamis), in 2010 in order to monitor seismicity, crustal deformations, and tsunamis. DONET system consists of totally 20 stations, which is composed of six kinds of sensors, including strong-motion seismometers　and quartz pressure gauges. Those stations are densely distributed with an average spatial interval of 15-20 km and cover near the trench axis to coastal areas. Observed data are transferred to a land station through a fiber-optical cable and then to JAMSTEC (Japan Agency for Marine-Earth Science and Technology) data management center through a private network in real time. After 2011 off the Pacific coast of Tohoku Earthquake, each local government close to Nankai Trough try to plan disaster prevention scheme. JAMSTEC will disseminate DONET data combined with research accomplishment so that they will be widely recognized as important earthquake information. In order to open DONET data observed for research to local government, we have developed a web application system, REIS　(Real-time Earthquake Information System). REIS is providing seismic waveform data to some local governments close to Nankai Trough as a pilot study. As soon as operation of DONET is ready, REIS will start full-scale operation. REIS can display seismic waveform data of DONET in real-time, users can select strong motion and pressure data, and configure the options of trace view arrangement, time scale, and amplitude. In addition to real-time monitoring, REIS can display past seismic waveform data and show earthquake epicenters on the map. In this presentation, we briefly introduce DONET system and then show our web　application system. We also discuss our future plans for further developments of REIS.

Verification and Validation Methodology of Real-Time Adaptive Neural Networks for Aerospace Applications

NASA Technical Reports Server (NTRS)

Gupta, Pramod; Loparo, Kenneth; Mackall, Dale; Schumann, Johann; Soares, Fola

2004-01-01

Recent research has shown that adaptive neural based control systems are very effective in restoring stability and control of an aircraft in the presence of damage or failures. The application of an adaptive neural network with a flight critical control system requires a thorough and proven process to ensure safe and proper flight operation. Unique testing tools have been developed as part of a process to perform verification and validation (V&V) of real time adaptive neural networks used in recent adaptive flight control system, to evaluate the performance of the on line trained neural networks. The tools will help in certification from FAA and will help in the successful deployment of neural network based adaptive controllers in safety-critical applications. The process to perform verification and validation is evaluated against a typical neural adaptive controller and the results are discussed.

Evaluation of Real-Time Ground-Based GPS Meteorology

NASA Astrophysics Data System (ADS)

Fang, P.; Bock, Y.; Gutman, S.

2003-04-01

We demonstrate and evaluate a system to estimate zenith tropospheric delays in real time (5-10 minute latency) based on the technique of instantaneous GPS positioning as described by Bock et al. [2000] using data from the Orange County Real Time GPS Network. OCRTN is an upgrade of a sub-network of SCIGN sites in southern California to low latency (1-2 sec), high-rate (1 Hz) data streaming. Currently, ten sites are streaming data (Ashtech binary MBEN format) by means of dedicated, point-to-point radio modems to a network hub that translates the asynchronous serial data to TCP/IP and onto a PC workstation residing on a local area network. Software residing on the PC allows multiple clients to access the raw data simultaneously though TCP/IP. One of the clients is a Geodetics RTD server that receives and archives (1) the raw 1 Hz network data, (2) estimates of instantaneous positions and zenith tropospheric delays, and (3) RINEX data to decimated to 30 seconds. The network is composed of ten sites. The distribution of nine of the sites approximates a right triangle with two 60 km legs, and a tenth site on Catalina Island a distance of about 50 km (over water) from the hypotenuse of the triangle. Relative zenith delays are estimated every second with a latency less than a second. Median values are computed at a user-specified interval (e.g., 10 minutes) with outliers greater than 4 times the interquartile range rejected. We describe the results with those generated by our operational system using the GAMIT software, with a latency of 30-60 minutes. Earlier results (from a similar network) comparing 30-minute median RTD values to GAMIT 30-minute estimates indicate that the two solutions differ by about 1 cm. We also describe our approach to determining absolute zenith delays. If an Internet connection is available we will present a real-time demonstration. [Bock, Y., R. Nikolaidis, P. J. de Jonge, and M. Bevis, Instantaneous resolution of crustal motion at medium distances with the Global Positioning System, J. Geophys. Res., 105, 28,223-28,254, 2000.

Networked Multimedia: Are We There Yet?

ERIC Educational Resources Information Center

Wyman, Bill

1997-01-01

Discusses the technological advances in electronic communication over the last 30 years. Touches on various real-time interactive multimedia communications, including video on demand, videocassettes, laser discs, CD-ROM, a history of networking, terminal/host and client/server networking, intraoperability and interoperability and multimedia…

Sharing Writing through Computer Networking.

ERIC Educational Resources Information Center

Fey, Marion H.

1997-01-01

Suggests computer networking can support the essential purposes of the collaborative-writing movement, offering opportunities for sharing writing. Notes that literacy teachers are exploring the connectivity of computer networking through numerous designs that use either real-time or asynchronous communication. Discusses new roles for students and…

Real-time community detection in full social networks on a laptop

PubMed Central

Chamberlain, Benjamin Paul; Levy-Kramer, Josh; Humby, Clive

2018-01-01

For a broad range of research and practical applications it is important to understand the allegiances, communities and structure of key players in society. One promising direction towards extracting this information is to exploit the rich relational data in digital social networks (the social graph). As global social networks (e.g., Facebook and Twitter) are very large, most approaches make use of distributed computing systems for this purpose. Distributing graph processing requires solving many difficult engineering problems, which has lead some researchers to look at single-machine solutions that are faster and easier to maintain. In this article, we present an approach for analyzing full social networks on a standard laptop, allowing for interactive exploration of the communities in the locality of a set of user specified query vertices. The key idea is that the aggregate actions of large numbers of users can be compressed into a data structure that encapsulates the edge weights between vertices in a derived graph. Local communities can be constructed by selecting vertices that are connected to the query vertices with high edge weights in the derived graph. This compression is robust to noise and allows for interactive queries of local communities in real-time, which we define to be less than the average human reaction time of 0.25s. We achieve single-machine real-time performance by compressing the neighborhood of each vertex using minhash signatures and facilitate rapid queries through Locality Sensitive Hashing. These techniques reduce query times from hours using industrial desktop machines operating on the full graph to milliseconds on standard laptops. Our method allows exploration of strongly associated regions (i.e., communities) of large graphs in real-time on a laptop. It has been deployed in software that is actively used by social network analysts and offers another channel for media owners to monetize their data, helping them to continue to provide free services that are valued by billions of people globally. PMID:29342158

Network-based real-time radiation monitoring system in Synchrotron Radiation Research Center.

PubMed

Sheu, R J; Wang, J P; Chen, C R; Liu, J; Chang, F D; Jiang, S H

2003-10-01

The real-time radiation monitoring system (RMS) in the Synchrotron Radiation Research Center (SRRC) has been upgraded significantly during the past years. The new framework of the RMS is built on the popular network technology, including Ethernet hardware connections and Web-based software interfaces. It features virtually no distance limitations, flexible and scalable equipment connections, faster response time, remote diagnosis, easy maintenance, as well as many graphic user interface software tools. This paper briefly describes the radiation environment in SRRC and presents the system configuration, basic functions, and some operational results of this real-time RMS. Besides the control of radiation exposures, it has been demonstrated that a variety of valuable information or correlations could be extracted from the measured radiation levels delivered by the RMS, including the changes of operating conditions, beam loss pattern, radiation skyshine, and so on. The real-time RMS can be conveniently accessed either using the dedicated client program or World Wide Web interface. The address of the Web site is http:// www-rms.srrc.gov.tw.

Popularity and Novelty Dynamics in Evolving Networks.

PubMed

Abbas, Khushnood; Shang, Mingsheng; Abbasi, Alireza; Luo, Xin; Xu, Jian Jun; Zhang, Yu-Xia

2018-04-20

Network science plays a big role in the representation of real-world phenomena such as user-item bipartite networks presented in e-commerce or social media platforms. It provides researchers with tools and techniques to solve complex real-world problems. Identifying and predicting future popularity and importance of items in e-commerce or social media platform is a challenging task. Some items gain popularity repeatedly over time while some become popular and novel only once. This work aims to identify the key-factors: popularity and novelty. To do so, we consider two types of novelty predictions: items appearing in the popular ranking list for the first time; and items which were not in the popular list in the past time window, but might have been popular before the recent past time window. In order to identify the popular items, a careful consideration of macro-level analysis is needed. In this work we propose a model, which exploits item level information over a span of time to rank the importance of the item. We considered ageing or decay effect along with the recent link-gain of the items. We test our proposed model on four various real-world datasets using four information retrieval based metrics.

Problematic use of social networking sites among urban school going teenagers.

PubMed

Meena, Parth Singh; Mittal, Pankaj Kumar; Solanki, Ram Kumar

2012-07-01

Social networking sites like Facebook, Orkut and Twitter are virtual communities where users can create individual public profiles, interact with real-life friends and meet other people based on shared interests. An exponential rise in usage of Social Networking Sites have been seen within the last few years. Their ease of use and immediate gratification effect on users has changed the way people in general and students in particular spend their time. Young adults, particularly teenagers tended to be unaware of just how much time they really spent on social networking sites. Negative correlates of Social Networking Sites usage include the decrease in real life social community participation and academic achievement, as well as relationship problems, each of which may be indicative of potential addiction. the aim of the study was to find out whether teenagers, specially those living in cities spend too much time on social networking websites. 200 subjects, both boys and girls were included in the cross sectional study who were given a 20 item Young's internet addiction test modified for social networking sites. The responses were analyzed using chi square test and Fisher's exact test. 24.74% of the students were having occasional or 'frequency' problems while 2.02% of them were experiencing severe problems due to excessive time spent using social networking sites. With the ever increasing popularity of social media, teenagers are devoting significant time to social networking on websites and are prone to get 'addicted' to such form of online social interaction.

Circuit-Detour Design and Implementation - Enhancing the Southern California's Seismic Network Reliability through Redundant Network Paths

NASA Astrophysics Data System (ADS)

Watkins, M.; Busby, R.; Rico, H.; Johnson, M.; Hauksson, E.

2003-12-01

We provide enhanced network robustness by apportioning redundant data communications paths for seismic stations in the field. By providing for more than one telemetry route, either physical or logical, network operators can improve availability of seismic data while experiencing occasional network outages, and also during the loss of key gateway interfaces such as a router or central processor. This is especially important for seismic stations in sparsely populated regions where a loss of a single site may result in a significant gap in the network's monitoring capability. A number of challenges arise in the application of a circuit-detour mechanism. One requirement is that it fits well within the existing framework of our real-time system processing. It is also necessary to craft a system that is not needlessly complex to maintain or implement, particularly during a crisis. The method that we use for circuit-detours does not require the reconfiguration of dataloggers or communications equipment in the field. Remote network configurations remain static, changes are only required at the central site. We have implemented standardized procedures to detour circuits on similar transport mediums, such as virtual circuits on the same leased line; as well as physically different communications pathways, such as a microwave link backed up by a leased line. The lessons learned from these improvements in reliability, and optimization efforts could be applied to other real-time seismic networks. A fundamental tenant of most seismic networks is that they are reliable and have a high percentage of real-time data availability. A reasonable way to achieve these expectations is to provide alternate means of delivering data to the central processing sites, with a simple method for utilizing these alternate paths.

Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System.

PubMed

Sheik, Sadique; Coath, Martin; Indiveri, Giacomo; Denham, Susan L; Wennekers, Thomas; Chicca, Elisabetta

2012-01-01

Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems.

Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System

PubMed Central

Sheik, Sadique; Coath, Martin; Indiveri, Giacomo; Denham, Susan L.; Wennekers, Thomas; Chicca, Elisabetta

2011-01-01

Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems. PMID:22347163

Flow Control and Routing in an Integrated Voice and Data Communication Network

DTIC Science & Technology

1981-08-01

require continuous and almost real - time delivery; they are very sensitive to delay. Data conversations, on the other hand, are generally intolerant of...packets arrive in time to be delivered to the sink. However, this is not the solution we seek. We have noted that voice conversations require almost real ...by long messages that require continuous real - time delivery; e.g. voice facsimile, video. Class II: characterized by short discrete messages that

Current status of the real-time processing of complex radar signatures

NASA Astrophysics Data System (ADS)

Clay, E.

The real-time processing technique developed by ONERA to characterize radar signatures at the Brahms station is described. This technique is used for the real-time analysis of the RCS of airframes and rotating parts, the one-dimensional tomography of aircraft, and the RCS of electromagnetic decoys. Using this technique, it is also possible to optimize the experimental parameters, i.e., the analysis band, the microwave-network gain, and the electromagnetic window of the analysis.

Temporal node centrality in complex networks

NASA Astrophysics Data System (ADS)

Kim, Hyoungshick; Anderson, Ross

2012-02-01

Many networks are dynamic in that their topology changes rapidly—on the same time scale as the communications of interest between network nodes. Examples are the human contact networks involved in the transmission of disease, ad hoc radio networks between moving vehicles, and the transactions between principals in a market. While we have good models of static networks, so far these have been lacking for the dynamic case. In this paper we present a simple but powerful model, the time-ordered graph, which reduces a dynamic network to a static network with directed flows. This enables us to extend network properties such as vertex degree, closeness, and betweenness centrality metrics in a very natural way to the dynamic case. We then demonstrate how our model applies to a number of interesting edge cases, such as where the network connectivity depends on a small number of highly mobile vertices or edges, and show that our centrality definition allows us to track the evolution of connectivity. Finally we apply our model and techniques to two real-world dynamic graphs of human contact networks and then discuss the implication of temporal centrality metrics in the real world.

A wearable biofeedback control system based body area network for freestyle swimming.

PubMed

Rui Li; Zibo Cai; WeeSit Lee; Lai, Daniel T H

2016-08-01

Wearable posture measurement units are capable of enabling real-time performance evaluation and providing feedback to end users. This paper presents a wearable feedback prototype designed for freestyle swimming with focus on trunk rotation measurement. The system consists of a nine-degree-of-freedom inertial sensor, which is built in a central data collection and processing unit, and two vibration motors for delivering real-time feedback. Theses devices form a fundamental body area network (BAN). In the experiment setup, four recreational swimmers were asked to do two sets of 4 x 25m freestyle swimming without and with feedback provided respectively. Results showed that real-time biofeedback mechanism improves swimmers kinematic performance by an average of 4.5% reduction in session time. Swimmers can gradually adapt to feedback signals, and the biofeedback control system can be employed in swimmers daily training for fitness maintenance.

Advanced visualization platform for surgical operating room coordination: distributed video board system.

PubMed

Hu, Peter F; Xiao, Yan; Ho, Danny; Mackenzie, Colin F; Hu, Hao; Voigt, Roger; Martz, Douglas

2006-06-01

One of the major challenges for day-of-surgery operating room coordination is accurate and timely situation awareness. Distributed and secure real-time status information is key to addressing these challenges. This article reports on the design and implementation of a passive status monitoring system in a 19-room surgical suite of a major academic medical center. Key design requirements considered included integrated real-time operating room status display, access control, security, and network impact. The system used live operating room video images and patient vital signs obtained through monitors to automatically update events and operating room status. Images were presented on a "need-to-know" basis, and access was controlled by identification badge authorization. The system delivered reliable real-time operating room images and status with acceptable network impact. Operating room status was visualized at 4 separate locations and was used continuously by clinicians and operating room service providers to coordinate operating room activities.

GraphStore: A Distributed Graph Storage System for Big Data Networks

ERIC Educational Resources Information Center

Martha, VenkataSwamy

2013-01-01

Networks, such as social networks, are a universal solution for modeling complex problems in real time, especially in the Big Data community. While previous studies have attempted to enhance network processing algorithms, none have paved a path for the development of a persistent storage system. The proposed solution, GraphStore, provides an…

Modernization of the Slovenian National Seismic Network

NASA Astrophysics Data System (ADS)

Vidrih, R.; Godec, M.; Gosar, A.; Sincic, P.; Tasic, I.; Zivcic, M.

2003-04-01

The Environmental Agency of the Republic of Slovenia, the Seismology Office is responsible for the fast and reliable information about earthquakes, originating in the area of Slovenia and nearby. In the year 2000 the project Modernization of the Slovenian National Seismic Network started. The purpose of a modernized seismic network is to enable fast and accurate automatic location of earthquakes, to determine earthquake parameters and to collect data of local, regional and global earthquakes. The modernized network will be finished in the year 2004 and will consist of 25 Q730 remote broadband data loggers based seismic station subsystems transmitting in real-time data to the Data Center in Ljubljana, where the Seismology Office is located. The remote broadband station subsystems include 16 surface broadband seismometers CMG-40T, 5 broadband seismometers CMG-40T with strong motion accelerographs EpiSensor, 4 borehole broadband seismometers CMG-40T, all with accurate timing provided by GPS receivers. The seismic network will cover the entire Slovenian territory, involving an area of 20,256 km2. The network is planned in this way; more seismic stations will be around bigger urban centres and in regions with greater vulnerability (NW Slovenia, Krsko Brezice region). By the end of the year 2002, three old seismic stations were modernized and ten new seismic stations were built. All seismic stations transmit data to UNIX-based computers running Antelope system software. The data is transmitted in real time using TCP/IP protocols over the Goverment Wide Area Network . Real-time data is also exchanged with seismic networks in the neighbouring countries, where the data are collected from the seismic stations, close to the Slovenian border. A typical seismic station consists of the seismic shaft with the sensor and the data acquisition system and, the service shaft with communication equipment (modem, router) and power supply with a battery box. which provides energy in case of mains failure. The data acquisition systems are recording continuous time-series sampled at 200 sps, 20 sps and 1sps.

A robust sound perception model suitable for neuromorphic implementation.

PubMed

Coath, Martin; Sheik, Sadique; Chicca, Elisabetta; Indiveri, Giacomo; Denham, Susan L; Wennekers, Thomas

2013-01-01

We have recently demonstrated the emergence of dynamic feature sensitivity through exposure to formative stimuli in a real-time neuromorphic system implementing a hybrid analog/digital network of spiking neurons. This network, inspired by models of auditory processing in mammals, includes several mutually connected layers with distance-dependent transmission delays and learning in the form of spike timing dependent plasticity, which effects stimulus-driven changes in the network connectivity. Here we present results that demonstrate that the network is robust to a range of variations in the stimulus pattern, such as are found in naturalistic stimuli and neural responses. This robustness is a property critical to the development of realistic, electronic neuromorphic systems. We analyze the variability of the response of the network to "noisy" stimuli which allows us to characterize the acuity in information-theoretic terms. This provides an objective basis for the quantitative comparison of networks, their connectivity patterns, and learning strategies, which can inform future design decisions. We also show, using stimuli derived from speech samples, that the principles are robust to other challenges, such as variable presentation rate, that would have to be met by systems deployed in the real world. Finally we demonstrate the potential applicability of the approach to real sounds.

IoT for Real-Time Measurement of High-Throughput Liquid Dispensing in Laboratory Environments.

PubMed

Shumate, Justin; Baillargeon, Pierre; Spicer, Timothy P; Scampavia, Louis

2018-04-01

Critical to maintaining quality control in high-throughput screening is the need for constant monitoring of liquid-dispensing fidelity. Traditional methods involve operator intervention with gravimetric analysis to monitor the gross accuracy of full plate dispenses, visual verification of contents, or dedicated weigh stations on screening platforms that introduce potential bottlenecks and increase the plate-processing cycle time. We present a unique solution using open-source hardware, software, and 3D printing to automate dispenser accuracy determination by providing real-time dispense weight measurements via a network-connected precision balance. This system uses an Arduino microcontroller to connect a precision balance to a local network. By integrating the precision balance as an Internet of Things (IoT) device, it gains the ability to provide real-time gravimetric summaries of dispensing, generate timely alerts when problems are detected, and capture historical dispensing data for future analysis. All collected data can then be accessed via a web interface for reviewing alerts and dispensing information in real time or remotely for timely intervention of dispense errors. The development of this system also leveraged 3D printing to rapidly prototype sensor brackets, mounting solutions, and component enclosures.

Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

PubMed

Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

2018-02-01

Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

ATM: The Key To Harnessing the Power of Networked Multimedia.

ERIC Educational Resources Information Center

Gross, Rod

1996-01-01

ATM (Asynchronous Transfer Mode) network technology handles the real-time continuous traffic flow necessary to support desktop multimedia applications. Describes network applications already used: desktop video collaboration, distance learning, and broadcasting video delivery. Examines the architecture of ATM technology, video delivery and sound…

Using Neural Networks in Decision Making for a Reconfigurable Electro Mechanical Actuator (EMA)

NASA Technical Reports Server (NTRS)

Latino, Carl D.

2001-01-01

The objectives of this project were to demonstrate applicability and advantages of a neural network approach for evaluating the performance of an electro-mechanical actuator (EMA). The EMA in question was intended for the X-37 Advanced Technology Vehicle. It will have redundant components for safety and reliability. The neural networks for this application are to monitor the operation of the redundant electronics that control the actuator in real time and decide on the operating configuration. The system we proposed consists of the actuator, sensors, control circuitry and dedicated (embedded) processors. The main purpose of the study was to develop suitable hardware and neural network capable of allowing real time reconfiguration decisions to be made. This approach was to be compared to other methods such as fuzzy logic and knowledge based systems considered for the same application. Over the course of the project a more general objective was the identification of the other neural network applications and the education of interested NASA personnel on the topic of Neural Networks.

Neural networks for simultaneous classification and parameter estimation in musical instrument control

NASA Astrophysics Data System (ADS)

Lee, Michael; Freed, Adrian; Wessel, David

1992-08-01

In this report we present our tools for prototyping adaptive user interfaces in the context of real-time musical instrument control. Characteristic of most human communication is the simultaneous use of classified events and estimated parameters. We have integrated a neural network object into the MAX language to explore adaptive user interfaces that considers these facets of human communication. By placing the neural processing in the context of a flexible real-time musical programming environment, we can rapidly prototype experiments on applications of adaptive interfaces and learning systems to musical problems. We have trained networks to recognize gestures from a Mathews radio baton, Nintendo Power GloveTM, and MIDI keyboard gestural input devices. In one experiment, a network successfully extracted classification and attribute data from gestural contours transduced by a continuous space controller, suggesting their application in the interpretation of conducting gestures and musical instrument control. We discuss network architectures, low-level features extracted for the networks to operate on, training methods, and musical applications of adaptive techniques.

A Real-Time Decision Support System for Voltage Collapse Avoidance in Power Supply Networks

NASA Astrophysics Data System (ADS)

Chang, Chen-Sung

This paper presents a real-time decision support system (RDSS) based on artificial intelligence (AI) for voltage collapse avoidance (VCA) in power supply networks. The RDSS scheme employs a fuzzy hyperrectangular composite neural network (FHRCNN) to carry out voltage risk identification (VRI). In the event that a threat to the security of the power supply network is detected, an evolutionary programming (EP)-based algorithm is triggered to determine the operational settings required to restore the power supply network to a secure condition. The effectiveness of the RDSS methodology is demonstrated through its application to the American Electric Power Provider System (AEP, 30-bus system) under various heavy load conditions and contingency scenarios. In general, the numerical results confirm the ability of the RDSS scheme to minimize the risk of voltage collapse in power supply networks. In other words, RDSS provides Power Provider Enterprises (PPEs) with a viable tool for performing on-line voltage risk assessment and power system security enhancement functions.

Verification of the Usefulness of the Trimble Rtx Extended Satellite Technology with the Xfill Function in the Local Network Implementing Rtk Measurements

NASA Astrophysics Data System (ADS)

Siejka, Zbigniew

2014-12-01

The paper presents the method of satellite measurements, which gives users the ability of GNSS continuous precise positioning in real time, even in the case of short interruptions in receiving the correction of the local ground system of measurements support. The proposed method is a combination of two satellite positioning technologies RTN GNSS and RTX Extended. In technology RTX Extended the xFill function was used for precise positioning in real time and in the local reference system. This function provides the ability to perform measurement without the need for constant communication with the ground support satellite system. Test measurements were performed on a test basis located in Krakow, and RTN GNSS positioning was done based on the national network of reference stations of the ASGEUPOS. The solution allows for short (up to 5 minutes) interruptions in radio or internet communication. When the primary stream of RTN correction is not available, then the global corrections Trimble xFill broadcasted by satellite are used. The new technology uses in the real-time data from the global network of tracking stations and contributes significantly to improving the quality and efficiency of surveying works. At present according to the authors, technology Trimble CenterPoint RTX can guarantee repeatability of measurements not worse than 3.8 cm (Trimble Survey Division, 2012). In the paper the comparative analysis of measurement results between the two technologies was performed: RTN carried out in the classic way, which was based on the corrections of the terrestrial local network of the Polish system of active geodetic network (ASG-EUPOS) and RTK xFill technology. The results were related to the data of test network, established as error free. The research gave satisfactory results and confirmed the great potential of the use of the new technology in the geodetic work realization. By combining these two technologies of GNSS surveying the user can greatly improve the overall performance of real-time positioning.

How to Handle a Satellite Change in an Operational TWSTFT Network?

DTIC Science & Technology

2010-11-01

42 nd Annual Precise Time and Time Interval (PTTI) Meeting 285 HOW TO HANDLE A SATELLITE CHANGE IN AN OPERATIONAL TWSTFT NETWORK...way satellite time and frequency transfer ( TWSTFT ) is a powerful technique because of its real-time capabilities. In principle, the time difference...between remote clocks is almost instantaneously known after a measurement session. Long-term TWSTFT operations have required changes between

The GFZ real-time GNSS precise positioning service system and its adaption for COMPASS

NASA Astrophysics Data System (ADS)

Li, Xingxing; Ge, Maorong; Zhang, Hongping; Nischan, Thomas; Wickert, Jens

2013-03-01

Motivated by the IGS real-time Pilot Project, GFZ has been developing its own real-time precise positioning service for various applications. An operational system at GFZ is now broadcasting real-time orbits, clocks, global ionospheric model, uncalibrated phase delays and regional atmospheric corrections for standard PPP, PPP with ambiguity fixing, single-frequency PPP and regional augmented PPP. To avoid developing various algorithms for different applications, we proposed a uniform algorithm and implemented it into our real-time software. In the new processing scheme, we employed un-differenced raw observations with atmospheric delays as parameters, which are properly constrained by real-time derived global ionospheric model or regional atmospheric corrections and by the empirical characteristics of the atmospheric delay variation in time and space. The positioning performance in terms of convergence time and ambiguity fixing depends mainly on the quality of the received atmospheric information and the spatial and temporal constraints. The un-differenced raw observation model can not only integrate PPP and NRTK into a seamless positioning service, but also syncretize these two techniques into a unique model and algorithm. Furthermore, it is suitable for both dual-frequency and sing-frequency receivers. Based on the real-time data streams from IGS, EUREF and SAPOS reference networks, we can provide services of global precise point positioning (PPP) with 5-10 cm accuracy, PPP with ambiguity-fixing of 2-5 cm accuracy, PPP using single-frequency receiver with accuracy of better than 50 cm and PPP with regional augmentation for instantaneous ambiguity resolution of 1-3 cm accuracy. We adapted the system for current COMPASS to provide PPP service. COMPASS observations from a regional network of nine stations are used for precise orbit determination and clock estimation in simulated real-time mode, the orbit and clock products are applied for real-time precise point positioning. The simulated real-time PPP service confirms that real-time positioning services of accuracy at dm-level and even cm-level is achievable with COMPASS only.

Research in Network Management Techniques for Tactical Data Communications Network.

DTIC Science & Technology

1982-09-01

the control period. Research areas include Packet Network modelling, adaptive network routing, network design algorithms, network design techniques...contro!lers are designed to perform their limited tasks optimally. For the dynamic routing problem considered here, the local controllers are node...feedback to finding in optimum stead-o-state routing (static strategies) under non - control which can be easily implemented in real time. congested

First field trial of Virtual Network Operator oriented network on demand (NoD) service provisioning over software defined multi-vendor OTN networks

NASA Astrophysics Data System (ADS)

Li, Yajie; Zhao, Yongli; Zhang, Jie; Yu, Xiaosong; Chen, Haoran; Zhu, Ruijie; Zhou, Quanwei; Yu, Chenbei; Cui, Rui

2017-01-01

A Virtual Network Operator (VNO) is a provider and reseller of network services from other telecommunications suppliers. These network providers are categorized as virtual because they do not own the underlying telecommunication infrastructure. In terms of business operation, VNO can provide customers with personalized services by leasing network infrastructure from traditional network providers. The unique business modes of VNO lead to the emergence of network on demand (NoD) services. The conventional network provisioning involves a series of manual operation and configuration, which leads to high cost in time. Considering the advantages of Software Defined Networking (SDN), this paper proposes a novel NoD service provisioning solution to satisfy the private network need of VNOs. The solution is first verified in the real software defined multi-domain optical networks with multi-vendor OTN equipment. With the proposed solution, NoD service can be deployed via online web portals in near-real time. It reinvents the customer experience and redefines how network services are delivered to customers via an online self-service portal. Ultimately, this means a customer will be able to simply go online, click a few buttons and have new services almost instantaneously.

In-Network Processing for Mission-Critical Wireless Networked Sensing and Control: A Real-Time, Efficiency, and Resiliency Perspective

ERIC Educational Resources Information Center

Xiang, Qiao

2014-01-01

As wireless cyber-physical systems (WCPS) are increasingly being deployed in mission-critical applications, it becomes imperative that we consider application QoS requirements in in-network processing (INP). In this dissertation, we explore the potentials of two INP methods, packet packing and network coding, on improving network performance while…

A Tree Based Self-routing Scheme for Mobility Support in Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Kim, Young-Duk; Yang, Yeon-Mo; Kang, Won-Seok; Kim, Jin-Wook; An, Jinung

Recently, WSNs (Wireless Sensor Networks) with mobile robot is a growing technology that offer efficient communication services for anytime and anywhere applications. However, the tiny sensor node has very limited network resources due to its low battery power, low data rate, node mobility, and channel interference constraint between neighbors. Thus, in this paper, we proposed a tree based self-routing protocol for autonomous mobile robots based on beacon mode and implemented in real test-bed environments. The proposed scheme offers beacon based real-time scheduling for reliable association process between parent and child nodes. In addition, it supports smooth handover procedure by reducing flooding overhead of control packets. Throughout the performance evaluation by using a real test-bed system and simulation, we illustrate that our proposed scheme demonstrates promising performance for wireless sensor networks with mobile robots.

Wireless in-situ Sensor Network for Agriculture and Water Monitoring on a River Basin Scale in Southern Finland: Evaluation from a Data User’s Perspective

PubMed Central

Kotamäki, Niina; Thessler, Sirpa; Koskiaho, Jari; Hannukkala, Asko O.; Huitu, Hanna; Huttula, Timo; Havento, Jukka; Järvenpää, Markku

2009-01-01

Sensor networks are increasingly being implemented for environmental monitoring and agriculture to provide spatially accurate and continuous environmental information and (near) real-time applications. These networks provide a large amount of data which poses challenges for ensuring data quality and extracting relevant information. In the present paper we describe a river basin scale wireless sensor network for agriculture and water monitoring. The network, called SoilWeather, is unique and the first of this type in Finland. The performance of the network is assessed from the user and maintainer perspectives, concentrating on data quality, network maintenance and applications. The results showed that the SoilWeather network has been functioning in a relatively reliable way, but also that the maintenance and data quality assurance by automatic algorithms and calibration samples requires a lot of effort, especially in continuous water monitoring over large areas. We see great benefits on sensor networks enabling continuous, real-time monitoring, while data quality control and maintenance efforts highlight the need for tight collaboration between sensor and sensor network owners to decrease costs and increase the quality of the sensor data in large scale applications. PMID:22574050

The use of UNIX in a real-time environment

NASA Technical Reports Server (NTRS)

Luken, R. D.; Simons, P. C.

1986-01-01

This paper describes a project to evaluate the feasibility of using commercial off-the-shelf hardware and the UNIX operating system, to implement a real-time control and monitor system. A functional subset of the Checkout, Control and Monitor System was chosen as the test bed for the project. The project consists of three separate architecture implementations: a local area bus network, a star network, and a central host. The motivation for this project stemmed from the need to find a way to implement real-time systems, without the cost burden of developing and maintaining custom hardware and unique software. This has always been accepted as the only option because of the need to optimize the implementation for performance. However, with the cost/performance of today's hardware, the inefficiencies of high-level languages and portable operating systems can be effectively overcome.

Real-time biomimetic Central Pattern Generators in an FPGA for hybrid experiments

PubMed Central

Ambroise, Matthieu; Levi, Timothée; Joucla, Sébastien; Yvert, Blaise; Saïghi, Sylvain

2013-01-01

This investigation of the leech heartbeat neural network system led to the development of a low resources, real-time, biomimetic digital hardware for use in hybrid experiments. The leech heartbeat neural network is one of the simplest central pattern generators (CPG). In biology, CPG provide the rhythmic bursts of spikes that form the basis for all muscle contraction orders (heartbeat) and locomotion (walking, running, etc.). The leech neural network system was previously investigated and this CPG formalized in the Hodgkin–Huxley neural model (HH), the most complex devised to date. However, the resources required for a neural model are proportional to its complexity. In response to this issue, this article describes a biomimetic implementation of a network of 240 CPGs in an FPGA (Field Programmable Gate Array), using a simple model (Izhikevich) and proposes a new synapse model: activity-dependent depression synapse. The network implementation architecture operates on a single computation core. This digital system works in real-time, requires few resources, and has the same bursting activity behavior as the complex model. The implementation of this CPG was initially validated by comparing it with a simulation of the complex model. Its activity was then matched with pharmacological data from the rat spinal cord activity. This digital system opens the way for future hybrid experiments and represents an important step toward hybridization of biological tissue and artificial neural networks. This CPG network is also likely to be useful for mimicking the locomotion activity of various animals and developing hybrid experiments for neuroprosthesis development. PMID:24319408

An advanced real-time digital signal processing system for linear systems emulation, with special emphasis on network and acoustic response characterization

NASA Astrophysics Data System (ADS)

Gaydecki, Patrick; Fernandes, Bosco

2003-11-01

A fast digital signal processing (DSP) system is described that can perform real-time emulation of a wide variety of linear audio-bandwidth systems and networks, such as reverberant spaces, musical instrument bodies and very high order filter networks. The hardware design is based upon a Motorola DSP56309 operating at 110 million multiplication-accumulations per second and a dual-channel 24 bit codec with a maximum sampling frequency of 192 kHz. High level software has been developed to express complex vector frequency responses as both infinite impulse response (IIR) and finite impulse response (FIR) coefficients, in a form suitable for real-time convolution by the firmware installed in the DSP system memory. An algorithm has also been devised to express IIR filters as equivalent FIR structures, thereby obviating the potential instabilities associated with recursive equations and negating the traditional deficiencies of FIR filters respecting equivalent analogue designs. The speed and dynamic range of the system is such that, when sampling at 48 kHz, the frequency response can be specified to a spectral precision of 22 Hz when sampling at 10 kHz, this resolution increases to 0.9 Hz. Moreover, it is also possible to control the phase of any frequency band with a theoretical precision of 10-5 degrees in all cases. The system has been applied in the study of analogue filter networks, real-time Hilbert transformation, phase-shift systems and musical instrument body emulation, where it is providing valuable new insights into the understanding of psychoacoustic mechanisms.

ISKANDARnet IOMOS: Near real-time equatorial space weather monitoring and alert system in Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Musa, Tajul Ariffin; Leong, Shien Kwun; Abdullah, Khairul Anuar; Othman, Rusli

2012-11-01

This work proposes ISKANDARnet Ionospheric Outburst MOnitoring and alert System (IOMOS), along with Ionospheric Outburst Index (IOX) to develop an operational near real-time space weather service for Malaysia. The IOMOS is based on Global Positioning System (GPS) Network-based Real-Time Kinematic (NRTK) concept which is by nature for atmospheric (ionosphere and troposphere) modeling within the network coverage. The elegance of this solution lies in the fact that IOMOS utilize differential ionospheric residual from network of GPS baselines which incur no additional cost for operation. Users will be informed about the ionospheric perturbation through Short Message Service (SMS), email or Twitter®. This approach will ultimately beneficial for the navigation and satellite positioning communities, particularly during the coming Solar Cycle 24. In addition, a combination of local and global GPS network has been employed to study the equatorial ionosphere geomorphology and climatology in the Malaysian sector. Equatorial Total Electron Content (TEC) over Malaysia shows semi-annual, annual, and seasonal variations with maximum values appearing during equinoctial months and minimum during solstices months. The TEC value during vernal equinox is about 21% higher than autumnal equinox, and December solstice exceeds that at the June solstice by around 14%. It is also found that semi-annual variation is present at all levels of solar activity, whereas June solstice predominates December solstice during high solar activity for annual and seasonal variations. In near future, a near real-time TEC derivation system will be developed to support equatorial ionosphere modeling to enhance space weather service for Malaysia.

The VLBA correlator: Real-time in the distributed era

NASA Technical Reports Server (NTRS)

Wells, D. C.

1992-01-01

The correlator is the signal processing engine of the Very Long Baseline Array (VLBA). Radio signals are recorded on special wideband (128 Mb/s) digital recorders at the 10 telescopes, with sampling times controlled by hydrogen maser clocks. The magnetic tapes are shipped to the Array Operations Center in Socorro, New Mexico, where they are played back simultaneously into the correlator. Real-time software and firmware controls the playback drives to achieve synchronization, compute models of the wavefront delay, control the numerous modules of the correlator, and record FITS files of the fringe visibilities at the back-end of the correlator. In addition to the more than 3000 custom VLSI chips which handle the massive data flow of the signal processing, the correlator contains a total of more than 100 programmable computers, 8-, 16- and 32-bit CPUs. Code is downloaded into front-end CPU's dependent on operating mode. Low-level code is assembly language, high-level code is C running under a RT OS. We use VxWorks on Motorola MVME147 CPU's. Code development is on a complex of SPARC workstations connected to the RT CPU's by Ethernet. The overall management of the correlation process is dependent on a database management system. We use Ingres running on a Sparcstation-2. We transfer logging information from the database of the VLBA Monitor and Control System to our database using Ingres/NET. Job scripts are computed and are transferred to the real-time computers using NFS, and correlation job execution logs and status flow back by the route. Operator status and control displays use windows on workstations, interfaced to the real-time processes by network protocols. The extensive network protocol support provided by VxWorks is invaluable. The VLBA Correlator's dependence on network protocols is an example of the radical transformation of the real-time world over the past five years. Real-time is becoming more like conventional computing. Paradoxically, 'conventional' computing is also adopting practices from the real-time world: semaphores, shared memory, light-weight threads, and concurrency. This appears to be a convergence of thinking.

Translating genomics into practice for real-time surveillance and response to carbapenemase-producing Enterobacteriaceae: evidence from a complex multi-institutional KPC outbreak.

PubMed

Kwong, Jason C; Lane, Courtney R; Romanes, Finn; Gonçalves da Silva, Anders; Easton, Marion; Cronin, Katie; Waters, Mary Jo; Tomita, Takehiro; Stevens, Kerrie; Schultz, Mark B; Baines, Sarah L; Sherry, Norelle L; Carter, Glen P; Mu, Andre; Sait, Michelle; Ballard, Susan A; Seemann, Torsten; Stinear, Timothy P; Howden, Benjamin P

2018-01-01

Until recently, Klebsiella pneumoniae carbapenemase (KPC)-producing Enterobacteriaceae were rarely identified in Australia. Following an increase in the number of incident cases across the state of Victoria, we undertook a real-time combined genomic and epidemiological investigation. The scope of this study included identifying risk factors and routes of transmission, and investigating the utility of genomics to enhance traditional field epidemiology for informing management of established widespread outbreaks. All KPC-producing Enterobacteriaceae isolates referred to the state reference laboratory from 2012 onwards were included. Whole-genome sequencing was performed in parallel with a detailed descriptive epidemiological investigation of each case, using Illumina sequencing on each isolate. This was complemented with PacBio long-read sequencing on selected isolates to establish high-quality reference sequences and interrogate characteristics of KPC-encoding plasmids. Initial investigations indicated that the outbreak was widespread, with 86 KPC-producing Enterobacteriaceae isolates ( K. pneumoniae 92%) identified from 35 different locations across metropolitan and rural Victoria between 2012 and 2015. Initial combined analyses of the epidemiological and genomic data resolved the outbreak into distinct nosocomial transmission networks, and identified healthcare facilities at the epicentre of KPC transmission. New cases were assigned to transmission networks in real-time, allowing focussed infection control efforts. PacBio sequencing confirmed a secondary transmission network arising from inter-species plasmid transmission. Insights from Bayesian transmission inference and analyses of within-host diversity informed the development of state-wide public health and infection control guidelines, including interventions such as an intensive approach to screening contacts following new case detection to minimise unrecognised colonisation. A real-time combined epidemiological and genomic investigation proved critical to identifying and defining multiple transmission networks of KPC Enterobacteriaceae, while data from either investigation alone were inconclusive. The investigation was fundamental to informing infection control measures in real-time and the development of state-wide public health guidelines on carbapenemase-producing Enterobacteriaceae surveillance and management.

New Method for Real Time Influenza Surveillance in Primary Care: A Wisconsin Research and Education Network (WREN) Supported Study.

PubMed

Temte, Jonathan L; Barlow, Shari; Schemmel, Amber; Temte, Emily; Hahn, David L; Reisdorf, Erik; Shult, Peter; Tamerius, John

2017-01-01

The goal of public health infectious disease surveillance systems is to provide accurate laboratory results in near-real time. When it comes to influenza surveillance, most current systems are encumbered with inherent delays encountered in the real-life chaos of medical practice. To combat this, we implemented and tested near-real-time surveillance using a rapid influenza detection test (RIDT) coupled with immediate, wireless transmission of results to public health entities. A network of 19 primary care clinics across Wisconsin were recruited, including 4 sites already involved in ongoing influenza surveillance and 15 sites that were new to surveillance activities. Each site was provided with a Quidel Sofia Influenza A+B RIDT analyzer attached to a wireless router. Influenza test results, along with patient age, were transmitted immediately to a cloud-based server, automatically compiled, and forwarded to the surveillance team daily. Weekly counts of positive influenza A and B cases were compared with positive polymerase chain reaction (PCR) detections from an independent surveillance system within the state. Following Institutional Review Board (IRB) and institutional approvals, we recruited 19 surveillance sites, installed equipment, and trained staff within 4 months. Of the 1119 cases tested between September 15, 2013 and June 28, 2014, 316 were positive for influenza. The system provided early detection of the influenza outbreak in Wisconsin. The influenza peak between January 12 and 25, 2014, as well as the epidemic curve, closely matched that derived from the established PCR laboratory network (r = 0.927; P < .001). A network of influenza RIDTs with wireless transmission of results approximated the long-sought-after goal of real-time influenza surveillance. Results from the initial year strongly support this approach to highly accurate and timely influenza surveillance. © Copyright 2017 by the American Board of Family Medicine.

Deep neural networks to enable real-time multimessenger astrophysics

NASA Astrophysics Data System (ADS)

George, Daniel; Huerta, E. A.

2018-02-01

Gravitational wave astronomy has set in motion a scientific revolution. To further enhance the science reach of this emergent field of research, there is a pressing need to increase the depth and speed of the algorithms used to enable these ground-breaking discoveries. We introduce Deep Filtering—a new scalable machine learning method for end-to-end time-series signal processing. Deep Filtering is based on deep learning with two deep convolutional neural networks, which are designed for classification and regression, to detect gravitational wave signals in highly noisy time-series data streams and also estimate the parameters of their sources in real time. Acknowledging that some of the most sensitive algorithms for the detection of gravitational waves are based on implementations of matched filtering, and that a matched filter is the optimal linear filter in Gaussian noise, the application of Deep Filtering using whitened signals in Gaussian noise is investigated in this foundational article. The results indicate that Deep Filtering outperforms conventional machine learning techniques, achieves similar performance compared to matched filtering, while being several orders of magnitude faster, allowing real-time signal processing with minimal resources. Furthermore, we demonstrate that Deep Filtering can detect and characterize waveform signals emitted from new classes of eccentric or spin-precessing binary black holes, even when trained with data sets of only quasicircular binary black hole waveforms. The results presented in this article, and the recent use of deep neural networks for the identification of optical transients in telescope data, suggests that deep learning can facilitate real-time searches of gravitational wave sources and their electromagnetic and astroparticle counterparts. In the subsequent article, the framework introduced herein is directly applied to identify and characterize gravitational wave events in real LIGO data.

Real-time processing of radar return on a parallel computer

NASA Technical Reports Server (NTRS)

Aalfs, David D.

1992-01-01

NASA is working with the FAA to demonstrate the feasibility of pulse Doppler radar as a candidate airborne sensor to detect low altitude windshears. The need to provide the pilot with timely information about possible hazards has motivated a demand for real-time processing of a radar return. Investigated here is parallel processing as a means of accommodating the high data rates required. A PC based parallel computer, called the transputer, is used to investigate issues in real time concurrent processing of radar signals. A transputer network is made up of an array of single instruction stream processors that can be networked in a variety of ways. They are easily reconfigured and software development is largely independent of the particular network topology. The performance of the transputer is evaluated in light of the computational requirements. A number of algorithms have been implemented on the transputers in OCCAM, a language specially designed for parallel processing. These include signal processing algorithms such as the Fast Fourier Transform (FFT), pulse-pair, and autoregressive modelling, as well as routing software to support concurrency. The most computationally intensive task is estimating the spectrum. Two approaches have been taken on this problem, the first and most conventional of which is to use the FFT. By using table look-ups for the basis function and other optimizing techniques, an algorithm has been developed that is sufficient for real time. The other approach is to model the signal as an autoregressive process and estimate the spectrum based on the model coefficients. This technique is attractive because it does not suffer from the spectral leakage problem inherent in the FFT. Benchmark tests indicate that autoregressive modeling is feasible in real time.

Composable Flexible Real-time Packet Scheduling for Networks on-Chip

DTIC Science & Technology

2012-05-16

unclassified b . ABSTRACT unclassified c. THIS PAGE unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 Copyright © 2012...words, real-time flows need to be composable. We set this as the design goal for our packet scheduling discipline developed in this paper. B . Motivating...with closest deadline is chosen to forward to the next router. B . Traffic Model We assume a traffic model for real-time flows similar to the one used

Asymptotic theory of time varying networks with burstiness and heterogeneous activation patterns

NASA Astrophysics Data System (ADS)

Burioni, Raffaella; Ubaldi, Enrico; Vezzani, Alessandro

2017-05-01

The recent availability of large-scale, time-resolved and high quality digital datasets has allowed for a deeper understanding of the structure and properties of many real-world networks. The empirical evidence of a temporal dimension prompted the switch of paradigm from a static representation of networks to a time varying one. In this work we briefly review the framework of time-varying-networks in real world social systems, especially focusing on the activity-driven paradigm. We develop a framework that allows for the encoding of three generative mechanisms that seem to play a central role in the social networks’ evolution: the individual’s propensity to engage in social interactions, its strategy in allocate these interactions among its alters and the burstiness of interactions amongst social actors. The functional forms and probability distributions encoding these mechanisms are typically data driven. A natural question arises if different classes of strategies and burstiness distributions, with different local scale behavior and analogous asymptotics can lead to the same long time and large scale structure of the evolving networks. We consider the problem in its full generality, by investigating and solving the system dynamics in the asymptotic limit, for general classes of ties allocation mechanisms and waiting time probability distributions. We show that the asymptotic network evolution is driven by a few characteristics of these functional forms, that can be extracted from direct measurements on large datasets.

Ranking in evolving complex networks

NASA Astrophysics Data System (ADS)

Liao, Hao; Mariani, Manuel Sebastian; Medo, Matúš; Zhang, Yi-Cheng; Zhou, Ming-Yang

2017-05-01

Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are allocated by companies and policymakers, among others. This calls for a deep understanding of how existing ranking algorithms perform, and which are their possible biases that may impair their effectiveness. Many popular ranking algorithms (such as Google's PageRank) are static in nature and, as a consequence, they exhibit important shortcomings when applied to real networks that rapidly evolve in time. At the same time, recent advances in the understanding and modeling of evolving networks have enabled the development of a wide and diverse range of ranking algorithms that take the temporal dimension into account. The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks. We emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of network traffic, prediction of future links, and identification of significant nodes.

Counting motifs in dynamic networks.

PubMed

Mukherjee, Kingshuk; Hasan, Md Mahmudul; Boucher, Christina; Kahveci, Tamer

2018-04-11

A network motif is a sub-network that occurs frequently in a given network. Detection of such motifs is important since they uncover functions and local properties of the given biological network. Finding motifs is however a computationally challenging task as it requires solving the costly subgraph isomorphism problem. Moreover, the topology of biological networks change over time. These changing networks are called dynamic biological networks. As the network evolves, frequency of each motif in the network also changes. Computing the frequency of a given motif from scratch in a dynamic network as the network topology evolves is infeasible, particularly for large and fast evolving networks. In this article, we design and develop a scalable method for counting the number of motifs in a dynamic biological network. Our method incrementally updates the frequency of each motif as the underlying network's topology evolves. Our experiments demonstrate that our method can update the frequency of each motif in orders of magnitude faster than counting the motif embeddings every time the network changes. If the network evolves more frequently, the margin with which our method outperforms the existing static methods, increases. We evaluated our method extensively using synthetic and real datasets, and show that our method is highly accurate(≥ 96%) and that it can be scaled to large dense networks. The results on real data demonstrate the utility of our method in revealing interesting insights on the evolution of biological processes.

Aggregated channels network for real-time pedestrian detection

NASA Astrophysics Data System (ADS)

Ghorban, Farzin; Marín, Javier; Su, Yu; Colombo, Alessandro; Kummert, Anton

2018-04-01

Convolutional neural networks (CNNs) have demonstrated their superiority in numerous computer vision tasks, yet their computational cost results prohibitive for many real-time applications such as pedestrian detection which is usually performed on low-consumption hardware. In order to alleviate this drawback, most strategies focus on using a two-stage cascade approach. Essentially, in the first stage a fast method generates a significant but reduced amount of high quality proposals that later, in the second stage, are evaluated by the CNN. In this work, we propose a novel detection pipeline that further benefits from the two-stage cascade strategy. More concretely, the enriched and subsequently compressed features used in the first stage are reused as the CNN input. As a consequence, a simpler network architecture, adapted for such small input sizes, allows to achieve real-time performance and obtain results close to the state-of-the-art while running significantly faster without the use of GPU. In particular, considering that the proposed pipeline runs in frame rate, the achieved performance is highly competitive. We furthermore demonstrate that the proposed pipeline on itself can serve as an effective proposal generator.

Rule-based simulation models

NASA Technical Reports Server (NTRS)

Nieten, Joseph L.; Seraphine, Kathleen M.

1991-01-01

Procedural modeling systems, rule based modeling systems, and a method for converting a procedural model to a rule based model are described. Simulation models are used to represent real time engineering systems. A real time system can be represented by a set of equations or functions connected so that they perform in the same manner as the actual system. Most modeling system languages are based on FORTRAN or some other procedural language. Therefore, they must be enhanced with a reaction capability. Rule based systems are reactive by definition. Once the engineering system has been decomposed into a set of calculations using only basic algebraic unary operations, a knowledge network of calculations and functions can be constructed. The knowledge network required by a rule based system can be generated by a knowledge acquisition tool or a source level compiler. The compiler would take an existing model source file, a syntax template, and a symbol table and generate the knowledge network. Thus, existing procedural models can be translated and executed by a rule based system. Neural models can be provide the high capacity data manipulation required by the most complex real time models.

Network-Based Real-time Integrated Fire Detection and Alarm (FDA) System with Building Automation

NASA Astrophysics Data System (ADS)

Anwar, F.; Boby, R. I.; Rashid, M. M.; Alam, M. M.; Shaikh, Z.

2017-11-01

Fire alarm systems have become increasingly an important lifesaving technology in many aspects, such as applications to detect, monitor and control any fire hazard. A large sum of money is being spent annually to install and maintain the fire alarm systems in buildings to protect property and lives from the unexpected spread of fire. Several methods are already developed and it is improving on a daily basis to reduce the cost as well as increase quality. An integrated Fire Detection and Alarm (FDA) systems with building automation was studied, to reduce cost and improve their reliability by preventing false alarm. This work proposes an improved framework for FDA system to ensure a robust intelligent network of FDA control panels in real-time. A shortest path algorithmic was chosen for series of buildings connected by fiber optic network. The framework shares information and communicates with each fire alarm panels connected in peer to peer configuration and declare the network state using network address declaration from any building connected in network. The fiber-optic connection was proposed to reduce signal noises, thus increasing large area coverage, real-time communication and long-term safety. Based on this proposed method an experimental setup was designed and a prototype system was developed to validate the performance in practice. Also, the distributed network system was proposed to connect with an optional remote monitoring terminal panel to validate proposed network performance and ensure fire survivability where the information is sequentially transmitted. The proposed FDA system is different from traditional fire alarm and detection system in terms of topology as it manages group of buildings in an optimal and efficient manner.Introduction

Packetized Video On MAGNET

NASA Astrophysics Data System (ADS)

Lazar, Aurel A.; White, John S.

1987-07-01

Theoretical analysis of integrated local area network model of MAGNET, an integrated network testbed developed at Columbia University, shows that the bandwidth freed up during video and voice calls during periods of little movement in the images and periods of silence in the speech signals could be utilized efficiently for graphics and data transmission. Based on these investigations, an architecture supporting adaptive protocols that are dynamicaly controlled by the requirements of a fluctuating load and changing user environment has been advanced. To further analyze the behavior of the network, a real-time packetized video system has been implemented. This system is embedded in the real-time multimedia workstation EDDY, which integrates video, voice, and data traffic flows. Protocols supporting variable-bandwidth, fixed-quality packetized video transport are described in detail.

Learning to train neural networks for real-world control problems

NASA Technical Reports Server (NTRS)

Feldkamp, Lee A.; Puskorius, G. V.; Davis, L. I., Jr.; Yuan, F.

1994-01-01

Over the past three years, our group has concentrated on the application of neural network methods to the training of controllers for real-world systems. This presentation describes our approach, surveys what we have found to be important, mentions some contributions to the field, and shows some representative results. Topics discussed include: (1) executing model studies as rehearsal for experimental studies; (2) the importance of correct derivatives; (3) effective training with second-order (DEKF) methods; (4) the efficacy of time-lagged recurrent networks; (5) liberation from the tyranny of the control cycle using asynchronous truncated backpropagation through time; and (6) multistream training for robustness. Results from model studies of automotive idle speed control serve as examples for several of these topics.

The new Langley Research Center advanced real-time simulation (ARTS) system

NASA Technical Reports Server (NTRS)

Crawford, D. J.; Cleveland, J. I., II

1986-01-01

Based on a survey of current local area network technology with special attention paid to high bandwidth and very low transport delay requirements, NASA's Langley Research Center designed a new simulation subsystem using the computer automated measurement and control (CAMAC) network. This required significant modifications to the standard CAMAC system and development of a network switch, a clocking system, new conversion equipment, new consoles, supporting software, etc. This system is referred to as the advanced real-time simulation (ARTS) system. It is presently being built at LaRC. This paper provides a functional and physical description of the hardware and a functional description of the software. The requirements which drove the design are presented as well as present performance figures and status.

Convergence of broadband optical and wireless access networks

NASA Astrophysics Data System (ADS)

Chang, Gee-Kung; Jia, Zhensheng; Chien, Hung-Chang; Chowdhury, Arshad; Hsueh, Yu-Ting; Yu, Jianjun

2009-01-01

This paper describes convergence of optical and wireless access networks for delivering high-bandwidth integrated services over optical fiber and air links. Several key system technologies are proposed and experimentally demonstrated. We report here, for the first ever, a campus-wide field trial demonstration of radio-over-fiber (RoF) system transmitting uncompressed standard-definition (SD) high-definition (HD) real-time video contents, carried by 2.4-GHz radio and 60- GHz millimeter-wave signals, respectively, over 2.5-km standard single mode fiber (SMF-28) through the campus fiber network at Georgia Institute of Technology (GT). In addition, subsystem technologies of Base Station and wireless tranceivers operated at 60 GHz for real-time video distribution have been developed and tested.

Characterizing time series: when Granger causality triggers complex networks

NASA Astrophysics Data System (ADS)

Ge, Tian; Cui, Yindong; Lin, Wei; Kurths, Jürgen; Liu, Chong

2012-08-01

In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIHMassachusetts Institute of Technology-Beth Israel Hospital. human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length.

ASSESSMENT OF AN ENSEMBLE OF SEVEN REAL-TIME OZONE FORECASTS OVER EASTERN NORTH AMERICA DURING THE SUMMER OF 2004

EPA Science Inventory

The real-time forecasts of ozone (O₃) from seven air quality forecast models (AQFMs) are statistically evaluated against observations collected during July and August of 2004 (53 days) through the Aerometric Information Retrieval Now (AIRNow) network at roughly 340 mon...

genRE: A Method to Extend Gridded Precipitation Climatology Data Sets in Near Real-Time for Hydrological Forecasting Purposes

NASA Astrophysics Data System (ADS)

van Osnabrugge, B.; Weerts, A. H.; Uijlenhoet, R.

2017-11-01

To enable operational flood forecasting and drought monitoring, reliable and consistent methods for precipitation interpolation are needed. Such methods need to deal with the deficiencies of sparse operational real-time data compared to quality-controlled offline data sources used in historical analyses. In particular, often only a fraction of the measurement network reports in near real-time. For this purpose, we present an interpolation method, generalized REGNIE (genRE), which makes use of climatological monthly background grids derived from existing gridded precipitation climatology data sets. We show how genRE can be used to mimic and extend climatological precipitation data sets in near real-time using (sparse) real-time measurement networks in the Rhine basin upstream of the Netherlands (approximately 160,000 km2). In the process, we create a 1.2 × 1.2 km transnational gridded hourly precipitation data set for the Rhine basin. Precipitation gauge data are collected, spatially interpolated for the period 1996-2015 with genRE and inverse-distance squared weighting (IDW), and then evaluated on the yearly and daily time scale against the HYRAS and EOBS climatological data sets. Hourly fields are compared qualitatively with RADOLAN radar-based precipitation estimates. Two sources of uncertainty are evaluated: station density and the impact of different background grids (HYRAS versus EOBS). The results show that the genRE method successfully mimics climatological precipitation data sets (HYRAS/EOBS) over daily, monthly, and yearly time frames. We conclude that genRE is a good interpolation method of choice for real-time operational use. genRE has the largest added value over IDW for cases with a low real-time station density and a high-resolution background grid.

Real-time Data Streams from ``e-RemoteCtrl'' to Central VLBI Network Status Monitoring Services Like IVS Live

NASA Astrophysics Data System (ADS)

Neidhardt, Alexander; Collioud, Arnaud

2014-12-01

A central VLBI network status monitoring can be realized by using online status information about current VLBI sessions, real-time, and status data directly from each radio telescope. Such monitoring helps to organize sessions or to get immediate feedback from the active telescopes. Therefore the remote control software for VLBI radio telescopes ``e-RemoteCtrl'' (http://www.econtrol-software.de), which enables remote access as extension to the NASA Field System, realizes real-time data streams to dedicated data centers. The software has direct access to the status information about the current observation (e.g., schedule, scan, source) and the telescope (e.g., current state, temperature, pressure) in real-time. This information are directly sent to ``IVS Live''. ``IVS Live'' (http://ivslive.obs.u-bordeaux1.fr/) is a Web tool that can be used to follow the observing sessions, organized by the International VLBI Service for Geodesy and Astrometry (IVS), navigate through past or upcoming sessions, or search and display specific information about sessions, sources (like VLBI images), and stations, by using an Internet browser.

SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound.

PubMed

Baumgartner, Christian F; Kamnitsas, Konstantinos; Matthew, Jacqueline; Fletcher, Tara P; Smith, Sandra; Koch, Lisa M; Kainz, Bernhard; Rueckert, Daniel

2017-11-01

Identifying and interpreting fetal standard scan planes during 2-D ultrasound mid-pregnancy examinations are highly complex tasks, which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks, which can automatically detect 13 fetal standard views in freehand 2-D ultrasound data as well as provide a localization of the fetal structures via a bounding box. An important contribution is that the network learns to localize the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localization task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localization on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modeling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localization task.

Architecture for an integrated real-time air combat and sensor network simulation

NASA Astrophysics Data System (ADS)

Criswell, Evans A.; Rushing, John; Lin, Hong; Graves, Sara

2007-04-01

An architecture for an integrated air combat and sensor network simulation is presented. The architecture integrates two components: a parallel real-time sensor fusion and target tracking simulation, and an air combat simulation. By integrating these two simulations, it becomes possible to experiment with scenarios in which one or both sides in a battle have very large numbers of primitive passive sensors, and to assess the likely effects of those sensors on the outcome of the battle. Modern Air Power is a real-time theater-level air combat simulation that is currently being used as a part of the USAF Air and Space Basic Course (ASBC). The simulation includes a variety of scenarios from the Vietnam war to the present day, and also includes several hypothetical future scenarios. Modern Air Power includes a scenario editor, an order of battle editor, and full AI customization features that make it possible to quickly construct scenarios for any conflict of interest. The scenario editor makes it possible to place a wide variety of sensors including both high fidelity sensors such as radars, and primitive passive sensors that provide only very limited information. The parallel real-time sensor network simulation is capable of handling very large numbers of sensors on a computing cluster of modest size. It can fuse information provided by disparate sensors to detect and track targets, and produce target tracks.

Staged Inference using Conditional Deep Learning for energy efficient real-time smart diagnosis.

PubMed

Parsa, Maryam; Panda, Priyadarshini; Sen, Shreyas; Roy, Kaushik

2017-07-01

Recent progress in biosensor technology and wearable devices has created a formidable opportunity for remote healthcare monitoring systems as well as real-time diagnosis and disease prevention. The use of data mining techniques is indispensable for analysis of the large pool of data generated by the wearable devices. Deep learning is among the promising methods for analyzing such data for healthcare applications and disease diagnosis. However, the conventional deep neural networks are computationally intensive and it is impractical to use them in real-time diagnosis with low-powered on-body devices. We propose Staged Inference using Conditional Deep Learning (SICDL), as an energy efficient approach for creating healthcare monitoring systems. For smart diagnostics, we observe that all diagnoses are not equally challenging. The proposed approach thus decomposes the diagnoses into preliminary analysis (such as healthy vs unhealthy) and detailed analysis (such as identifying the specific type of cardio disease). The preliminary diagnosis is conducted real-time with a low complexity neural network realized on the resource-constrained on-body device. The detailed diagnosis requires a larger network that is implemented remotely in cloud and is conditionally activated only for detailed diagnosis (unhealthy individuals). We evaluated the proposed approach using available physiological sensor data from Physionet databases, and achieved 38% energy reduction in comparison to the conventional deep learning approach.

Optimization of active distribution networks: Design and analysis of significative case studies for enabling control actions of real infrastructure

NASA Astrophysics Data System (ADS)

Moneta, Diana; Mora, Paolo; Viganò, Giacomo; Alimonti, Gianluca

2014-12-01

The diffusion of Distributed Generation (DG) based on Renewable Energy Sources (RES) requires new strategies to ensure reliable and economic operation of the distribution networks and to support the diffusion of DG itself. An advanced algorithm (DISCoVER - DIStribution Company VoltagE Regulator) is being developed to optimize the operation of active network by means of an advanced voltage control based on several regulations. Starting from forecasted load and generation, real on-field measurements, technical constraints and costs for each resource, the algorithm generates for each time period a set of commands for controllable resources that guarantees achievement of technical goals minimizing the overall cost. Before integrating the controller into the telecontrol system of the real networks, and in order to validate the proper behaviour of the algorithm and to identify possible critical conditions, a complete simulation phase has started. The first step is concerning the definition of a wide range of "case studies", that are the combination of network topology, technical constraints and targets, load and generation profiles and "costs" of resources that define a valid context to test the algorithm, with particular focus on battery and RES management. First results achieved from simulation activity on test networks (based on real MV grids) and actual battery characteristics are given, together with prospective performance on real case applications.

The Canarian Seismic Monitoring Network: design, development and first result

NASA Astrophysics Data System (ADS)

D'Auria, Luca; Barrancos, José; Padilla, Germán D.; García-Hernández, Rubén; Pérez, Aaron; Pérez, Nemesio M.

2017-04-01

Tenerife is an active volcanic island which experienced several eruptions of moderate intensity in historical times, and few explosive eruptions in the Holocene. The increasing population density and the consistent number of tourists are constantly raising the volcanic risk. In June 2016 Instituto Volcanologico de Canarias started the deployment of a seismological volcano monitoring network consisting of 15 broadband seismic stations. The network began its full operativity in November 2016. The aim of the network are both volcano monitoring and scientific research. Currently data are continuously recorded and processed in real-time. Seismograms, hypocentral parameters, statistical informations about the seismicity and other data are published on a web page. We show the technical characteristics of the network and an estimate of its detection threshold and earthquake location performances. Furthermore we present other near-real time procedures on the data: analysis of the ambient noise for determining the shallow velocity model and temporal velocity variations, detection of earthquake multiplets through massive data mining of the seismograms and automatic relocation of events through double-difference location.

Embedded Electro-Optic Sensor Network for the On-Site Calibration and Real-Time Performance Monitoring of Large-Scale Phased Arrays

DTIC Science & Technology

2005-07-09

This final report summarizes the progress during the Phase I SBIR project entitled Embedded Electro - Optic Sensor Network for the On-Site Calibration...network based on an electro - optic field-detection technique (the Electro - optic Sensor Network, or ESN) for the performance evaluation of phased

Rank-dependent deactivation in network evolution.

PubMed

Xu, Xin-Jian; Zhou, Ming-Chen

2009-12-01

A rank-dependent deactivation mechanism is introduced to network evolution. The growth dynamics of the network is based on a finite memory of individuals, which is implemented by deactivating one site at each time step. The model shows striking features of a wide range of real-world networks: power-law degree distribution, high clustering coefficient, and disassortative degree correlation.

Automatic Detection of Nausea Using Bio-Signals During Immerging in A Virtual Reality Environment

DTIC Science & Technology

2001-10-25

reduce the redundancy in those parameters, and constructed an artificial neural network with those principal components. Using the network we constructed, we could partially detect nausea in real time.

Real-time transmission of full-motion echocardiography over a high-speed data network: impact of data rate and network quality of service.

PubMed

Main, M L; Foltz, D; Firstenberg, M S; Bobinsky, E; Bailey, D; Frantz, B; Pleva, D; Baldizzi, M; Meyers, D P; Jones, K; Spence, M C; Freeman, K; Morehead, A; Thomas, J D

2000-08-01

With high-resolution network transmission required for telemedicine, education, and guided-image acquisition, the impact of errors and transmission rates on image quality needs evaluation. We transmitted clinical echocardiograms from 2 National Aeronautics and Space Administration (NASA) research centers with the use of Motion Picture Expert Group-2 (MPEG-2) encoding and asynchronous transmission mode (ATM) network protocol over the NASA Research and Education Network. Data rates and network quality (cell losses [CLR], errors [CER], and delay variability [CVD]) were altered and image quality was judged. At speeds of 3 to 5 megabits per second (Mbps), digital images were superior to those on videotape; at 2 Mbps, images were equivalent. Increasing CLR caused occasional, brief pauses. Extreme CER and CDV increases still yielded high-quality images. Real-time echocardiographic acquisition, guidance, and transmission is feasible with the use of MPEG-2 and ATM with broadcast quality seen above 3 Mbps, even with severe network quality degradation. These techniques can be applied to telemedicine and used for planned echocardiography aboard the International Space Station.

Real-time transmission of full-motion echocardiography over a high-speed data network: impact of data rate and network quality of service

NASA Technical Reports Server (NTRS)

Main, M. L.; Foltz, D.; Firstenberg, M. S.; Bobinsky, E.; Bailey, D.; Frantz, B.; Pleva, D.; Baldizzi, M.; Meyers, D. P.; Jones, K.;

EMERGEncy ID NET: Review of a 20-Year Multisite Emergency Department Emerging Infections Research Network

PubMed Central

Santibanez, Scott; Fischer, Leah S; Krishnadasan, Anusha; Sederdahl, Bethany; Merlin, Toby; Moran, Gregory J; Talan, David A; Mower, William; Sullivan, Matthew; Abrahamian, Fredrick M; Ong, Sam; Gross, Eric; Salhi, Bisan; Heilpern, Katherine; Hess, Jeremy; Karras, David; Biros, Michelle; Dunbar, Lala; Takhar, Sukhjit; Pollack, Charles; Runge, Jeffrey; Cheney, Paul; Rothrock, Stephen; O’Brian, John; Citron, Diane; Goldstein, Ellie; Finegold, Sydney; Nakase, Janet; Newdow, Michael; Merchant, Guy; Pathmarajah, Kavitha; Gonzalez, Eva; Mulrow, Mary; Bussman, Silas; Kalugdnan, Vernon; Peterson, Stephen; Pitts, Seth; Narayan, Kamil; Rubin, Ada; Kemble, Laurie; Beckham, Danielle; Neal, Niccole; Yagapen, Annick; Von Hofen, Carol; Hatala, Kathleen; Fuentes, Shelley; Sibley, Debbi; Colucci, Ashley; Hernandez, Jackeline; Cruse, Hope; Usher, Sarah; Hendrickson, Audrey; Dehnkamp, Kimberly; Zeglin, Britney; Jambaulikar, Guruprasad; Gorwitz, Rachel; Limbago, Brandi; Kuehnert, Matthew; Jarvis, William; Slutsker, Larry; Arvay, Melissa; Conn, Laura

2017-01-01

Abstract As providers of frontline clinical care for patients with acute and potentially life-threatening infections, emergency departments (EDs) have the priorities of saving lives and providing care quickly and efficiently. Although these facilities see a diversity of patients 24 hours per day and can collect prospective data in real time, their ability to conduct timely research on infectious syndromes is not well recognized. EMERGEncy ID NET is a national network that demonstrates that EDs can also collect data and conduct research in real time. This network collaborates with the Centers for Disease Control and Prevention (CDC) and other partners to study and address a wide range of infectious diseases and clinical syndromes. In this paper, we review selected highlights of EMERGEncy ID NET’s history from 1995 to 2017. We focus on the establishment of this multisite research network and the network’s collaborative research on a wide range of ED clinical topics. PMID:29670931

TreeMAC: Localized TDMA MAC protocol for real-time high-data-rate sensor networks

USGS Publications Warehouse

Song, W.-Z.; Huang, R.; Shirazi, B.; Husent, R.L.

2009-01-01

Earlier sensor network MAC protocols focus on energy conservation in low-duty cycle applications, while some recent applications involve real-time high-data-rate signals. This motivates us to design an innovative localized TDMA MAC protocol to achieve high throughput and low congestion in data collection sensor networks, besides energy conservation. TreeMAC divides a time cycle into frames and frame into slots. Parent determines children's frame assigmnent based on their relative bandwidth demand, and each node calculates its own slot assignment based on its hop-count to the sink. This innovative 2-dimensional frame-slot assignment algorithm has the following nice theory properties. Firstly, given any node, at any time slot, there is at most one active sender in its neighborhood (includ ing itself). Secondly, the packet scheduling with TreelMAC is bufferless, which therefore minimizes the probability of network congestion. Thirdly, the data throughput to gateway is at least 1/3 of the optimum assuming reliable links. Our experiments on a 24 node test bed demonstrate that TreeMAC protocol significantly improves network throughput and energy efficiency, by comparing to the TinyOS's default CSMA MAC protocol and a recent TDMA MAC protocol Funneling-MAC[8]. ?? 2009 IEEE.

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.

Complexity Optimization and High-Throughput Low-Latency Hardware Implementation of a Multi-Electrode Spike-Sorting Algorithm

PubMed Central

Dragas, Jelena; Jäckel, David; Hierlemann, Andreas; Franke, Felix

2017-01-01

Reliable real-time low-latency spike sorting with large data throughput is essential for studies of neural network dynamics and for brain-machine interfaces (BMIs), in which the stimulation of neural networks is based on the networks' most recent activity. However, the majority of existing multi-electrode spike-sorting algorithms are unsuited for processing high quantities of simultaneously recorded data. Recording from large neuronal networks using large high-density electrode sets (thousands of electrodes) imposes high demands on the data-processing hardware regarding computational complexity and data transmission bandwidth; this, in turn, entails demanding requirements in terms of chip area, memory resources and processing latency. This paper presents computational complexity optimization techniques, which facilitate the use of spike-sorting algorithms in large multi-electrode-based recording systems. The techniques are then applied to a previously published algorithm, on its own, unsuited for large electrode set recordings. Further, a real-time low-latency high-performance VLSI hardware architecture of the modified algorithm is presented, featuring a folded structure capable of processing the activity of hundreds of neurons simultaneously. The hardware is reconfigurable “on-the-fly” and adaptable to the nonstationarities of neuronal recordings. By transmitting exclusively spike time stamps and/or spike waveforms, its real-time processing offers the possibility of data bandwidth and data storage reduction. PMID:25415989

Complexity optimization and high-throughput low-latency hardware implementation of a multi-electrode spike-sorting algorithm.

PubMed

Dragas, Jelena; Jackel, David; Hierlemann, Andreas; Franke, Felix

2015-03-01

Reliable real-time low-latency spike sorting with large data throughput is essential for studies of neural network dynamics and for brain-machine interfaces (BMIs), in which the stimulation of neural networks is based on the networks' most recent activity. However, the majority of existing multi-electrode spike-sorting algorithms are unsuited for processing high quantities of simultaneously recorded data. Recording from large neuronal networks using large high-density electrode sets (thousands of electrodes) imposes high demands on the data-processing hardware regarding computational complexity and data transmission bandwidth; this, in turn, entails demanding requirements in terms of chip area, memory resources and processing latency. This paper presents computational complexity optimization techniques, which facilitate the use of spike-sorting algorithms in large multi-electrode-based recording systems. The techniques are then applied to a previously published algorithm, on its own, unsuited for large electrode set recordings. Further, a real-time low-latency high-performance VLSI hardware architecture of the modified algorithm is presented, featuring a folded structure capable of processing the activity of hundreds of neurons simultaneously. The hardware is reconfigurable “on-the-fly” and adaptable to the nonstationarities of neuronal recordings. By transmitting exclusively spike time stamps and/or spike waveforms, its real-time processing offers the possibility of data bandwidth and data storage reduction.

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.

EPMOSt: An Energy-Efficient Passive Monitoring System for Wireless Sensor Networks

PubMed Central

Garcia, Fernando P.; Andrade, Rossana M. C.; Oliveira, Carina T.; de Souza, José Neuman

2014-01-01

Monitoring systems are important for debugging and analyzing Wireless Sensor Networks (WSN). In passive monitoring, a monitoring network needs to be deployed in addition to the network to be monitored, named the target network. The monitoring network captures and analyzes packets transmitted by the target network. An energy-efficient passive monitoring system is necessary when we need to monitor a WSN in a real scenario because the lifetime of the monitoring network is extended and, consequently, the target network benefits from the monitoring for a longer time. In this work, we have identified, analyzed and compared the main passive monitoring systems proposed for WSN. During our research, we did not identify any passive monitoring system for WSN that aims to reduce the energy consumption of the monitoring network. Therefore, we propose an Energy-efficient Passive MOnitoring SysTem for WSN named EPMOSt that provides monitoring information using a Simple Network Management Protocol (SNMP) agent. Thus, any management tool that supports the SNMP protocol can be integrated with this monitoring system. Experiments with real sensors were performed in several scenarios. The results obtained show the energy efficiency of the proposed monitoring system and the viability of using it to monitor WSN in real scenarios. PMID:24949639

Hardware Realization of an Ethernet Packet Analyzer Search Engine

DTIC Science & Technology

2000-06-30

specific for the home automation industry. This analyzer will be at the gateway of a network and analyze Ethernet packets as they go by. It will keep... home automation and not the computer network. This system is a stand-alone real-time network analyzer capable of decoding Ethernet protocols. The

47 CFR 90.1407 - Spectrum use in the network.

Code of Federal Regulations, 2012 CFR

2012-10-01

... exclusion and/or immediate preemption of any commercial use on a dynamic, real-time priority basis, and to... network. (a) Spectrum use. The Shared Wireless Broadband Network will operate using spectrum associated... Block licensee and the Operating Company for the entire remaining term of the Public Safety Broadband...

Improved Adjoint-Operator Learning For A Neural Network

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad; Barhen, Jacob

1995-01-01

Improved method of adjoint-operator learning reduces amount of computation and associated computational memory needed to make electronic neural network learn temporally varying pattern (e.g., to recognize moving object in image) in real time. Method extension of method described in "Adjoint-Operator Learning for a Neural Network" (NPO-18352).

Country-wide rainfall maps from cellular communication networks

PubMed Central

Overeem, Aart; Leijnse, Hidde; Uijlenhoet, Remko

2013-01-01

Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal’s attenuation between transmitter and receiver. Here, we show how one such a network can be used to retrieve the space–time dynamics of rainfall for an entire country (The Netherlands, ∼35,500 km2), based on an unprecedented number of links (∼2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrates the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent. PMID:23382210

Recurrence network measures for hypothesis testing using surrogate data: Application to black hole light curves

NASA Astrophysics Data System (ADS)

Jacob, Rinku; Harikrishnan, K. P.; Misra, R.; Ambika, G.

2018-01-01

Recurrence networks and the associated statistical measures have become important tools in the analysis of time series data. In this work, we test how effective the recurrence network measures are in analyzing real world data involving two main types of noise, white noise and colored noise. We use two prominent network measures as discriminating statistic for hypothesis testing using surrogate data for a specific null hypothesis that the data is derived from a linear stochastic process. We show that the characteristic path length is especially efficient as a discriminating measure with the conclusions reasonably accurate even with limited number of data points in the time series. We also highlight an additional advantage of the network approach in identifying the dimensionality of the system underlying the time series through a convergence measure derived from the probability distribution of the local clustering coefficients. As examples of real world data, we use the light curves from a prominent black hole system and show that a combined analysis using three primary network measures can provide vital information regarding the nature of temporal variability of light curves from different spectroscopic classes.

Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data

PubMed Central

Liu, Hui; Zhang, Fan; Mishra, Shital Kumar; Zhou, Shuigeng; Zheng, Jie

2016-01-01

Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that our method can model temporally-ordered experimental observations. We evaluated the proposed method on a synthetic dataset and two real phosphoproteomic datasets. The experimental results demonstrate that our model can uncover drug-induced alterations in signaling pathways in cancer cells. Compared with existing hybrid models, our method can model feedback loops so that the dynamical mechanisms of signaling networks can be uncovered from time-series data. By calibrating generic models of signaling pathways against real data, our method supports precise predictions of context-specific anticancer drug effects, which is an important step towards precision medicine. PMID:27774993

Real-Time, Interactive Echocardiography Over High-Speed Networks: Feasibility and Functional Requirements

NASA Technical Reports Server (NTRS)

Bobinsky, Eric A.

1998-01-01

Real-time, Interactive Echocardiography Over High Speed Networks: Feasibility and Functional Requirements is an experiment in advanced telemedicine being conducted jointly by the NASA Lewis Research Center, the NASA Ames Research Center, and the Cleveland Clinic Foundation. In this project, a patient undergoes an echocardiographic examination in Cleveland while being diagnosed remotely by a cardiologist in California viewing a real-time display of echocardiographic video images transmitted over the broadband NASA Research and Education Network (NREN). The remote cardiologist interactively guides the sonographer administering the procedure through a two-way voice link between the two sites. Echocardiography is a noninvasive medical technique that applies ultrasound imaging to the heart, providing a "motion picture" of the heart in action. Normally, echocardiographic examinations are performed by a sonographer and cardiologist who are located in the same medical facility as the patient. The goal of telemedicine is to allow medical specialists to examine patients located elsewhere, typically in remote or medically underserved geographic areas. For example, a small, rural clinic might have access to an echocardiograph machine but not a cardiologist. By connecting this clinic to a major metropolitan medical facility through a communications network, a minimally trained technician would be able to carry out the procedure under the supervision and guidance of a qualified cardiologist.

Adaptive Resource Utilization Prediction System for Infrastructure as a Service Cloud.

PubMed

Zia Ullah, Qazi; Hassan, Shahzad; Khan, Gul Muhammad

2017-01-01

Infrastructure as a Service (IaaS) cloud provides resources as a service from a pool of compute, network, and storage resources. Cloud providers can manage their resource usage by knowing future usage demand from the current and past usage patterns of resources. Resource usage prediction is of great importance for dynamic scaling of cloud resources to achieve efficiency in terms of cost and energy consumption while keeping quality of service. The purpose of this paper is to present a real-time resource usage prediction system. The system takes real-time utilization of resources and feeds utilization values into several buffers based on the type of resources and time span size. Buffers are read by R language based statistical system. These buffers' data are checked to determine whether their data follows Gaussian distribution or not. In case of following Gaussian distribution, Autoregressive Integrated Moving Average (ARIMA) is applied; otherwise Autoregressive Neural Network (AR-NN) is applied. In ARIMA process, a model is selected based on minimum Akaike Information Criterion (AIC) values. Similarly, in AR-NN process, a network with the lowest Network Information Criterion (NIC) value is selected. We have evaluated our system with real traces of CPU utilization of an IaaS cloud of one hundred and twenty servers.

Adaptive Resource Utilization Prediction System for Infrastructure as a Service Cloud

PubMed Central

Hassan, Shahzad; Khan, Gul Muhammad

2017-01-01

Infrastructure as a Service (IaaS) cloud provides resources as a service from a pool of compute, network, and storage resources. Cloud providers can manage their resource usage by knowing future usage demand from the current and past usage patterns of resources. Resource usage prediction is of great importance for dynamic scaling of cloud resources to achieve efficiency in terms of cost and energy consumption while keeping quality of service. The purpose of this paper is to present a real-time resource usage prediction system. The system takes real-time utilization of resources and feeds utilization values into several buffers based on the type of resources and time span size. Buffers are read by R language based statistical system. These buffers' data are checked to determine whether their data follows Gaussian distribution or not. In case of following Gaussian distribution, Autoregressive Integrated Moving Average (ARIMA) is applied; otherwise Autoregressive Neural Network (AR-NN) is applied. In ARIMA process, a model is selected based on minimum Akaike Information Criterion (AIC) values. Similarly, in AR-NN process, a network with the lowest Network Information Criterion (NIC) value is selected. We have evaluated our system with real traces of CPU utilization of an IaaS cloud of one hundred and twenty servers. PMID:28811819

Problematic use of social networking sites among urban school going teenagers

PubMed Central

Meena, Parth Singh; Mittal, Pankaj Kumar; Solanki, Ram Kumar

2012-01-01

Background: Social networking sites like Facebook, Orkut and Twitter are virtual communities where users can create individual public profiles, interact with real-life friends and meet other people based on shared interests. An exponential rise in usage of Social Networking Sites have been seen within the last few years. Their ease of use and immediate gratification effect on users has changed the way people in general and students in particular spend their time. Young adults, particularly teenagers tended to be unaware of just how much time they really spent on social networking sites. Negative correlates of Social Networking Sites usage include the decrease in real life social community participation and academic achievement, as well as relationship problems, each of which may be indicative of potential addiction. Aims: the aim of the study was to find out whether teenagers, specially those living in cities spend too much time on social networking websites. Materials and Methods: 200 subjects, both boys and girls were included in the cross sectional study who were given a 20 item Young's internet addiction test modified for social networking sites. The responses were analyzed using chi square test and Fisher's exact test. Results: 24.74% of the students were having occasional or ‘frequency’ problems while 2.02% of them were experiencing severe problems due to excessive time spent using social networking sites. Conclusion: With the ever increasing popularity of social media, teenagers are devoting significant time to social networking on websites and are prone to get ‘addicted’ to such form of online social interaction. PMID:24250039

The NASA Real Time Mission Monitor - A Situational Awareness Tool for Conducting Tropical Cyclone Field Experiments

NASA Technical Reports Server (NTRS)

Goodman, Michael; Blakeslee, Richard; Hall, John; Parker, Philip; He, Yubin

2008-01-01

The NASA Real Time Mission Monitor (RTMM) is a situational awareness tool that integrates satellite, aircraft state information, airborne and surface instruments, and weather state data in to a single visualization package for real time field experiment management. RTMM optimizes science and logistic decision-making during field experiments by presenting timely data and graphics to the users to improve real time situational awareness of the experiment's assets. The RTMM is proven in the field as it supported program managers, scientists, and aircraft personnel during the NASA African Monsoon Multidisciplinary Analyses (investigated African easterly waves and Tropical Storm Debby and Helene) during August-September 2006 in Cape Verde, the Tropical Composition, Cloud and Climate Coupling experiment during July-August 2007 in Costa Rica, and the Hurricane Aerosonde mission into Hurricane Noel in 2-3 November 2007. The integration and delivery of this information is made possible through data acquisition systems, network communication links, and network server resources built and managed by collaborators at NASA Marshall Space Flight Center (MSFC) and Dryden Flight Research Center (DFRC). RTMM is evolving towards a more flexible and dynamic combination of sensor ingest, network computing, and decision-making activities through the use of a service oriented architecture based on community standards and protocols. Each field experiment presents unique challenges and opportunities for advancing the functionality of RTMM. A description of RTMM, the missions it has supported, and its new features that are under development will be presented.

Sensium: an ultra-low-power wireless body sensor network platform: design & application challenges.

PubMed

Wong, A W; McDonagh, D; Omeni, O; Nunn, C; Hernandez-Silveira, M; Burdett, A J

2009-01-01

In this paper we present a system-on-chip for wireless body sensor networks, which integrates a transceiver, hardware MAC protocol, microprocessor, IO peripherals, memories, ADC and custom sensor interfaces. Addressing the challenges in the design, this paper will continue to discuss the issues in the applications of this technology to body worn monitoring for real-time measurement of ECG, heart rate, physical activity, respiration and/or skin temperature. Two application challenges are described; the real-time measurement of energy expenditure using the LifePebble, and; the development issues surrounding the 'Digital Patch'.

Application and API for Real-time Visualization of Ground-motions and Tsunami

NASA Astrophysics Data System (ADS)

Aoi, S.; Kunugi, T.; Suzuki, W.; Kubo, T.; Nakamura, H.; Azuma, H.; Fujiwara, H.

2015-12-01

Due to the recent progress of seismograph and communication environment, real-time and continuous ground-motion observation becomes technically and economically feasible. K-NET and KiK-net, which are nationwide strong motion networks operated by NIED, cover all Japan by about 1750 stations in total. More than half of the stations transmit the ground-motion indexes and/or waveform data in every second. Traditionally, strong-motion data were recorded by event-triggering based instruments with non-continues telephone line which is connected only after an earthquake. Though the data from such networks mainly contribute to preparations for future earthquakes, huge amount of real-time data from dense network are expected to directly contribute to the mitigation of ongoing earthquake disasters through, e.g., automatic shutdown plants and helping decision-making for initial response. By generating the distribution map of these indexes and uploading them to the website, we implemented the real-time ground motion monitoring system, Kyoshin (strong-motion in Japanese) monitor. This web service (www.kyoshin.bosai.go.jp) started in 2008 and anyone can grasp the current ground motions of Japan. Though this service provides only ground-motion map in GIF format, to take full advantage of real-time strong-motion data to mitigate the ongoing disasters, digital data are important. We have developed a WebAPI to provide real-time data and related information such as ground motions (5 km-mesh) and arrival times estimated from EEW (earthquake early warning). All response data from this WebAPI are in JSON format and are easy to parse. We also developed Kyoshin monitor application for smartphone, 'Kmoni view' using the API. In this application, ground motions estimated from EEW are overlapped on the map with the observed one-second-interval indexes. The application can playback previous earthquakes for demonstration or disaster drill. In mobile environment, data traffic and battery are limited and it is not practical to regularly visualize all the data. The application has automatic starting (pop-up) function triggered by EEW. Similar WebAPI and application for tsunami are being prepared using the pressure data recorded by dense offshore observation network (S-net), which is under construction along the Japan Trench.

Internet Teleprescence by Real-Time View-Dependent Image Generation with Omnidirectional Video Camera

NASA Astrophysics Data System (ADS)

Morita, Shinji; Yamazawa, Kazumasa; Yokoya, Naokazu

2003-01-01

This paper describes a new networked telepresence system which realizes virtual tours into a visualized dynamic real world without significant time delay. Our system is realized by the following three steps: (1) video-rate omnidirectional image acquisition, (2) transportation of an omnidirectional video stream via internet, and (3) real-time view-dependent perspective image generation from the omnidirectional video stream. Our system is applicable to real-time telepresence in the situation where the real world to be seen is far from an observation site, because the time delay from the change of user"s viewing direction to the change of displayed image is small and does not depend on the actual distance between both sites. Moreover, multiple users can look around from a single viewpoint in a visualized dynamic real world in different directions at the same time. In experiments, we have proved that the proposed system is useful for internet telepresence.

Bulgarian National Digital Seismological Network

NASA Astrophysics Data System (ADS)

Dimitrova, L.; Solakov, D.; Nikolova, S.; Stoyanov, S.; Simeonova, S.; Zimakov, L. G.; Khaikin, L.

2011-12-01

The Bulgarian National Digital Seismological Network (BNDSN) consists of a National Data Center (NDC), 13 stations equipped with RefTek High Resolution Broadband Seismic Recorders - model DAS 130-01/3, 1 station equipped with Quanterra 680 and broadband sensors and accelerometers. Real-time data transfer from seismic stations to NDC is realized via Virtual Private Network of the Bulgarian Telecommunication Company. The communication interruptions don't cause any data loss at the NDC. The data are backed up in the field station recorder's 4Mb RAM memory and are retransmitted to the NDC immediately after the communication link is re-established. The recorders are equipped with 2 compact flash disks able to save more than 1 month long data. The data from the flash disks can be downloaded remotely using FTP. The data acquisition and processing hardware redundancy at the NDC is achieved by two clustered SUN servers and two Blade Workstations. To secure the acquisition, processing and data storage processes a three layer local network is designed at the NDC. Real-time data acquisition is performed using REFTEK's full duplex error-correction protocol RTPD. Data from the Quanterra recorder and foreign stations are fed into RTPD in real-time via SeisComP/SeedLink protocol. Using SeisComP/SeedLink software the NDC transfers real-time data to INGV-Roma, NEIC-USA, ORFEUS Data Center. Regional real-time data exchange with Romania, Macedonia, Serbia and Greece is established at the NDC also. Data processing is performed by the Seismic Network Data Processor (SNDP) software package running on the both Servers. SNDP includes subsystems: Real-time subsystem (RTS_SNDP) - for signal detection; evaluation of the signal parameters; phase identification and association; source estimation; Seismic analysis subsystem (SAS_SNDP) - for interactive data processing; Early warning subsystem (EWS_SNDP) - based on the first arrived P-phases. The signal detection process is performed by traditional STA/LTA detection algorithm. The filter parameters of the detectors are defined on the base of previously evaluated ambient noise at the seismic stations. Some extra modules for network command/control, state-of-health network monitoring and data archiving are running as well in the National Data Center. Three types of archives are produced in the NDC - two continuous - miniSEED format and RefTek PASSCAL format; and one event oriented in CSS3.0 scheme format. Modern digital equipment and broad-band seismometers installed at Bulgarian seismic stations, careful selection of the software packages for automatic and interactive data processing in the data center proved to be suitable choice for the purposes of BNDSN and NDC: ? to ensure reliable automatic localization of the seismic events and rapid notification of the governmental authorities in case of felt earthquakes on the territory of Bulgaria; ? to provide a modern basis for seismological studies in Bulgaria.

Multisensor Network System for Wildfire Detection Using Infrared Image Processing

PubMed Central

Bosch, I.; Serrano, A.; Vergara, L.

2013-01-01

This paper presents the next step in the evolution of multi-sensor wireless network systems in the early automatic detection of forest fires. This network allows remote monitoring of each of the locations as well as communication between each of the sensors and with the control stations. The result is an increased coverage area, with quicker and safer responses. To determine the presence of a forest wildfire, the system employs decision fusion in thermal imaging, which can exploit various expected characteristics of a real fire, including short-term persistence and long-term increases over time. Results from testing in the laboratory and in a real environment are presented to authenticate and verify the accuracy of the operation of the proposed system. The system performance is gauged by the number of alarms and the time to the first alarm (corresponding to a real fire), for different probability of false alarm (PFA). The necessity of including decision fusion is thereby demonstrated. PMID:23843734

Multisensor network system for wildfire detection using infrared image processing.

PubMed

Bosch, I; Serrano, A; Vergara, L

2013-01-01

This paper presents the next step in the evolution of multi-sensor wireless network systems in the early automatic detection of forest fires. This network allows remote monitoring of each of the locations as well as communication between each of the sensors and with the control stations. The result is an increased coverage area, with quicker and safer responses. To determine the presence of a forest wildfire, the system employs decision fusion in thermal imaging, which can exploit various expected characteristics of a real fire, including short-term persistence and long-term increases over time. Results from testing in the laboratory and in a real environment are presented to authenticate and verify the accuracy of the operation of the proposed system. The system performance is gauged by the number of alarms and the time to the first alarm (corresponding to a real fire), for different probability of false alarm (PFA). The necessity of including decision fusion is thereby demonstrated.

Mapping dynamic social networks in real life using participants' own smartphones.

PubMed

Boonstra, Tjeerd W; E Larsen, Mark; Christensen, Helen

2015-11-01

Interpersonal relationships are vital for our daily functioning and wellbeing. Social networks may form the primary means by which environmental influences determine individual traits. Several studies have shown the influence of social networks on decision-making, behaviors and wellbeing. Smartphones have great potential for measuring social networks in a real world setting. Here we tested the feasibility of using people's own smartphones as a data collection platform for face-to-face interactions. We developed an application for iOS and Android to collect Bluetooth data and acquired one week of data from 14 participants in our organization. The Bluetooth scanning statistics were used to quantify the time-resolved connection strength between participants and define the weights of a dynamic social network. We used network metrics to quantify changes in network topology over time and non-negative matrix factorization to identify cliques or subgroups that reoccurred during the week. The scanning rate varied considerably between smartphones running Android and iOS and egocentric networks metrics were correlated with the scanning rate. The time courses of two identified subgroups matched with two meetings that took place that week. These findings demonstrate the feasibility of using participants' own smartphones to map social network, whilst identifying current limitations of using generic smartphones. The bias introduced by variations in scanning rate and missing data is an important limitation that needs to be addressed in future studies.

Temporal coding in a silicon network of integrate-and-fire neurons.

PubMed

Liu, Shih-Chii; Douglas, Rodney

2004-09-01

Spatio-temporal processing of spike trains by neuronal networks depends on a variety of mechanisms distributed across synapses, dendrites, and somata. In natural systems, the spike trains and the processing mechanisms cohere though their common physical instantiation. This coherence is lost when the natural system is encoded for simulation on a general purpose computer. By contrast, analog VLSI circuits are, like neurons, inherently related by their real-time physics, and so, could provide a useful substrate for exploring neuronlike event-based processing. Here, we describe a hybrid analog-digital VLSI chip comprising a set of integrate-and-fire neurons and short-term dynamical synapses that can be configured into simple network architectures with some properties of neocortical neuronal circuits. We show that, despite considerable fabrication variance in the properties of individual neurons, the chip offers a viable substrate for exploring real-time spike-based processing in networks of neurons.

Design a Wearable Device for Blood Oxygen Concentration and Temporal Heart Beat Rate

NASA Astrophysics Data System (ADS)

Myint, Cho Zin; Barsoum, Nader; Ing, Wong Kiing

2010-06-01

The wireless network technology is increasingly important in healthcare as a result of the aging population and the tendency to acquire chronic disease such as heart attack, high blood pressure amongst the elderly. A wireless sensor network system that has the capability to monitor physiological sign such as SpO2 (Saturation of Arterial Oxygen) and heart beat rate in real-time from the human's body is highlighted in this study. This research is to design a prototype sensor network hardware, which consists of microcontroller PIC18F series and transceiver unit. The sensor is corporate into a wearable body sensor network which is small in size and easy to use. The sensor allows a non invasive, real time method to provide information regarding the health of the body. This enables a more efficient and economical means for managing the health care of the population.

Right-side-stretched multifractal spectra indicate small-worldness in networks

NASA Astrophysics Data System (ADS)

Oświȩcimka, Paweł; Livi, Lorenzo; Drożdż, Stanisław

2018-04-01

Complex network formalism allows to explain the behavior of systems composed by interacting units. Several prototypical network models have been proposed thus far. The small-world model has been introduced to mimic two important features observed in real-world systems: i) local clustering and ii) the possibility to move across a network by means of long-range links that significantly reduce the characteristic path length. A natural question would be whether there exist several ;types; of small-world architectures, giving rise to a continuum of models with properties (partially) shared with other models belonging to different network families. Here, we take advantage of the interplay between network theory and time series analysis and propose to investigate small-world signatures in complex networks by analyzing multifractal characteristics of time series generated from such networks. In particular, we suggest that the degree of right-sided asymmetry of multifractal spectra is linked with the degree of small-worldness present in networks. This claim is supported by numerical simulations performed on several parametric models, including prototypical small-world networks, scale-free, fractal and also real-world networks describing protein molecules. Our results also indicate that right-sided asymmetry emerges with the presence of the following topological properties: low edge density, low average shortest path, and high clustering coefficient.

A two-hop based adaptive routing protocol for real-time wireless sensor networks.

PubMed

Rachamalla, Sandhya; Kancherla, Anitha Sheela

2016-01-01

One of the most important and challenging issues in wireless sensor networks (WSNs) is to optimally manage the limited energy of nodes without degrading the routing efficiency. In this paper, we propose an energy-efficient adaptive routing mechanism for WSNs, which saves energy of nodes by removing the much delayed packets without degrading the real-time performance of the used routing protocol. It uses the adaptive transmission power algorithm which is based on the attenuation of the wireless link to improve the energy efficiency. The proposed routing mechanism can be associated with any geographic routing protocol and its performance is evaluated by integrating with the well known two-hop based real-time routing protocol, PATH and the resulting protocol is energy-efficient adaptive routing protocol (EE-ARP). The EE-ARP performs well in terms of energy consumption, deadline miss ratio, packet drop and end-to-end delay.

Real time selective harmonic minimization for multilevel inverters using genetic algorithm and artifical neural network angle generation

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

Filho, Faete J; Tolbert, Leon M; Ozpineci, Burak

2012-01-01

The work developed here proposes a methodology for calculating switching angles for varying DC sources in a multilevel cascaded H-bridges converter. In this approach the required fundamental is achieved, the lower harmonics are minimized, and the system can be implemented in real time with low memory requirements. Genetic algorithm (GA) is the stochastic search method to find the solution for the set of equations where the input voltages are the known variables and the switching angles are the unknown variables. With the dataset generated by GA, an artificial neural network (ANN) is trained to store the solutions without excessive memorymore » storage requirements. This trained ANN then senses the voltage of each cell and produces the switching angles in order to regulate the fundamental at 120 V and eliminate or minimize the low order harmonics while operating in real time.« less

Implementation of a real-time multi-channel gateway server in ubiquitous integrated biotelemetry system for emergency care (UIBSEC).

PubMed

Cheon, Gyeongwoo; Shin, Il Hyung; Jung, Min Yang; Kim, Hee Chan

2009-01-01

We developed a gateway server to support various types of bio-signal monitoring devices for ubiquitous emergency healthcare in a reliable, effective, and scalable way. The server provides multiple channels supporting real-time N-to-N client connections. We applied our system to four types of health monitoring devices including a 12-channel electrocardiograph (ECG), oxygen saturation (SpO(2)), and medical imaging devices (a ultrasonograph and a digital skin microscope). Different types of telecommunication networks were tested: WIBRO, CDMA, wireless LAN, and wired internet. We measured the performance of our system in terms of the transmission rate and the number of simultaneous connections. The results show that the proposed network communication strategy can be successfully applied to the ubiquitous emergency healthcare service by providing a fast rate enough for real-time video transmission and multiple connections among patients and medical personnel.

Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

NASA Astrophysics Data System (ADS)

Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

2018-02-01

Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

Methods and decision making on a Mars rover for identification of fossils

NASA Technical Reports Server (NTRS)

Eberlein, Susan; Yates, Gigi

1989-01-01

A system for automated fusion and interpretation of image data from multiple sensors, including multispectral data from an imaging spectrometer is being developed. Classical artificial intelligence techniques and artificial neural networks are employed to make real time decision based on current input and known scientific goals. Emphasis is placed on identifying minerals which could indicate past life activity or an environment supportive of life. Multispectral data can be used for geological analysis because different minerals have characteristic spectral reflectance in the visible and near infrared range. Classification of each spectrum into a broad class, based on overall spectral shape and locations of absorption bands is possible in real time using artificial neural networks. The goal of the system is twofold: multisensor and multispectral data must be interpreted in real time so that potentially interesting sites can be flagged and investigated in more detail while the rover is near those sites; and the sensed data must be reduced to the most compact form possible without loss of crucial information. Autonomous decision making will allow a rover to achieve maximum scientific benefit from a mission. Both a classical rule based approach and a decision neural network for making real time choices are being considered. Neural nets may work well for adaptive decision making. A neural net can be trained to work in two steps. First, the actual input state is mapped to the closest of a number of memorized states. After weighing the importance of various input parameters, the net produces an output decision based on the matched memory state. Real time, autonomous image data analysis and decision making capabilities are required for achieving maximum scientific benefit from a rover mission. The system under development will enhance the chances of identifying fossils or environments capable of supporting life on Mars

Developments in real-time monitoring for geologic hazard warnings (Invited)

NASA Astrophysics Data System (ADS)

Leith, W. S.; Mandeville, C. W.; Earle, P. S.

2013-12-01

Real-time data from global, national and local sensor networks enable prompt alerts and warnings of earthquakes, tsunami, volcanic eruptions, geomagnetic storms , broad-scale crustal deformation and landslides. State-of-the-art seismic systems can locate and evaluate earthquake sources in seconds, enabling 'earthquake early warnings' to be broadcast ahead of the damaging surface waves so that protective actions can be taken. Strong motion monitoring systems in buildings now support near-real-time structural damage detection systems, and in quiet times can be used for state-of-health monitoring. High-rate GPS data are being integrated with seismic strong motion data, allowing accurate determination of earthquake displacements in near-real time. GPS data, combined with rainfall, groundwater and geophone data, are now used for near-real-time landslide monitoring and warnings. Real-time sea-floor water pressure data are key for assessing tsunami generation by large earthquakes. For monitoring remote volcanoes that lack local ground-based instrumentation, the USGS uses new technologies such as infrasound arrays and the worldwide lightning detection array to detect eruptions in progress. A new real-time UV-camera system for measuring the two dimensional SO2 flux from volcanic plumes will allow correlations with other volcano monitoring data streams to yield fundamental data on changes in gas flux as an eruption precursor, and how magmas de-gas prior to and during eruptions. Precision magnetic field data support the generation of real-time indices of geomagnetic disturbances (Dst, K and others), and can be used to model electrical currents in the crust and bulk power system. Ground-induced electrical current monitors are used to track those currents so that power grids can be effectively managed during geomagnetic storms. Beyond geophysical sensor data, USGS is using social media to rapidly detect possible earthquakes and to collect firsthand accounts of the impacts of natural disasters useful for social science studies. Monitoring of tweets in real-time, when analyzed statistically and geographically, can give a prompt indication of an earthquake, well before seismic networks in sparsely instrumented regions can locate an event and determine its magnitude. With more and more real-time data becoming available, new applications and products are inevitable.

Connecting the snowpack to the internet of things: an IPv6 architecture for providing real-time measurements of hydrologic systems

NASA Astrophysics Data System (ADS)

Kerkez, B.; Zhang, Z.; Oroza, C.; Glaser, S. D.; Bales, R. C.

2012-12-01

We describe our improved, robust, and scalable architecture by which to rapidly instrument large-scale watersheds, while providing the resulting data in real-time. Our system consists of more than twenty wireless sensor networks and thousands of sensors, which will be deployed in the American River basin (5000 sq. km) of California. The core component of our system is known as a mote, a tiny, ultra-low-power, embedded wireless computer that can be used for any number of sensing applications. Our new generation of motes is equipped with IPv6 functionality, effectively giving each sensor in the field its own unique IP address, thus permitting users to remotely interact with the devices without going through intermediary services. Thirty to fifty motes will be deployed across 1-2 square kilometer regions to form a mesh-based wireless sensor network. Redundancy of local wireless links will ensure that data will always be able to traverse the network, even if hash wintertime conditions adversely affect some network nodes. These networks will be used to develop spatial estimates of a number of hydrologic parameters, focusing especially on snowpack. Each wireless sensor network has one main network controller, which is responsible with interacting with an embedded Linux computer to relay information across higher-powered, long-range wireless links (cell modems, satellite, WiFi) to neighboring networks and remote, offsite servers. The network manager is also responsible for providing an Internet connection to each mote. Data collected by the sensors can either be read directly by remote hosts, or stored on centralized servers for future access. With 20 such networks deployed in the American River, our system will comprise an unprecedented cyber-physical architecture for measuring hydrologic parameters in large-scale basins. The spatiotemporal density and real-time nature of the data is also expected to significantly improve operational hydrology and water resource management in the basin.

Burstiness and tie activation strategies in time-varying social networks.

PubMed

Ubaldi, Enrico; Vezzani, Alessandro; Karsai, Márton; Perra, Nicola; Burioni, Raffaella

2017-04-13

The recent developments in the field of social networks shifted the focus from static to dynamical representations, calling for new methods for their analysis and modelling. Observations in real social systems identified two main mechanisms that play a primary role in networks' evolution and influence ongoing spreading processes: the strategies individuals adopt when selecting between new or old social ties, and the bursty nature of the social activity setting the pace of these choices. We introduce a time-varying network model accounting both for ties selection and burstiness and we analytically study its phase diagram. The interplay of the two effects is non trivial and, interestingly, the effects of burstiness might be suppressed in regimes where individuals exhibit a strong preference towards previously activated ties. The results are tested against numerical simulations and compared with two empirical datasets with very good agreement. Consequently, the framework provides a principled method to classify the temporal features of real networks, and thus yields new insights to elucidate the effects of social dynamics on spreading processes.

Complex Dynamical Networks Constructed with Fully Controllable Nonlinear Nanomechanical Oscillators.

PubMed

Fon, Warren; Matheny, Matthew H; Li, Jarvis; Krayzman, Lev; Cross, Michael C; D'Souza, Raissa M; Crutchfield, James P; Roukes, Michael L

2017-10-11

Control of the global parameters of complex networks has been explored experimentally in a variety of contexts. Yet, the more difficult prospect of realizing arbitrary network architectures, especially analog physical networks that provide dynamical control of individual nodes and edges, has remained elusive. Given the vast hierarchy of time scales involved, it also proves challenging to measure a complex network's full internal dynamics. These span from the fastest nodal dynamics to very slow epochs over which emergent global phenomena, including network synchronization and the manifestation of exotic steady states, eventually emerge. Here, we demonstrate an experimental system that satisfies these requirements. It is based upon modular, fully controllable, nonlinear radio frequency nanomechanical oscillators, designed to form the nodes of complex dynamical networks with edges of arbitrary topology. The dynamics of these oscillators and their surrounding network are analog and continuous-valued and can be fully interrogated in real time. They comprise a piezoelectric nanomechanical membrane resonator, which serves as the frequency-determining element within an electrical feedback circuit. This embodiment permits network interconnections entirely within the electrical domain and provides unprecedented node and edge control over a vast region of parameter space. Continuous measurement of the instantaneous amplitudes and phases of every constituent oscillator node are enabled, yielding full and detailed network data without reliance upon statistical quantities. We demonstrate the operation of this platform through the real-time capture of the dynamics of a three-node ring network as it evolves from the uncoupled state to full synchronization.

Sonification of network traffic flow for monitoring and situational awareness

PubMed Central

2018-01-01

Maintaining situational awareness of what is happening within a computer network is challenging, not only because the behaviour happens within machines, but also because data traffic speeds and volumes are beyond human ability to process. Visualisation techniques are widely used to present information about network traffic dynamics. Although they provide operators with an overall view and specific information about particular traffic or attacks on the network, they often still fail to represent the events in an understandable way. Also, because they require visual attention they are not well suited to continuous monitoring scenarios in which network administrators must carry out other tasks. Here we present SoNSTAR (Sonification of Networks for SiTuational AwaReness), a real-time sonification system for monitoring computer networks to support network administrators’ situational awareness. SoNSTAR provides an auditory representation of all the TCP/IP traffic within a network based on the different traffic flows between between network hosts. A user study showed that SoNSTAR raises situational awareness levels by enabling operators to understand network behaviour and with the benefit of lower workload demands (as measured by the NASA TLX method) than visual techniques. SoNSTAR identifies network traffic features by inspecting the status flags of TCP/IP packet headers. Combinations of these features define particular traffic events which are mapped to recorded sounds to generate a soundscape that represents the real-time status of the network traffic environment. The sequence, timing, and loudness of the different sounds allow the network to be monitored and anomalous behaviour to be detected without the need to continuously watch a monitor screen. PMID:29672543

Sonification of network traffic flow for monitoring and situational awareness.

PubMed

Debashi, Mohamed; Vickers, Paul

2018-01-01

Maintaining situational awareness of what is happening within a computer network is challenging, not only because the behaviour happens within machines, but also because data traffic speeds and volumes are beyond human ability to process. Visualisation techniques are widely used to present information about network traffic dynamics. Although they provide operators with an overall view and specific information about particular traffic or attacks on the network, they often still fail to represent the events in an understandable way. Also, because they require visual attention they are not well suited to continuous monitoring scenarios in which network administrators must carry out other tasks. Here we present SoNSTAR (Sonification of Networks for SiTuational AwaReness), a real-time sonification system for monitoring computer networks to support network administrators' situational awareness. SoNSTAR provides an auditory representation of all the TCP/IP traffic within a network based on the different traffic flows between between network hosts. A user study showed that SoNSTAR raises situational awareness levels by enabling operators to understand network behaviour and with the benefit of lower workload demands (as measured by the NASA TLX method) than visual techniques. SoNSTAR identifies network traffic features by inspecting the status flags of TCP/IP packet headers. Combinations of these features define particular traffic events which are mapped to recorded sounds to generate a soundscape that represents the real-time status of the network traffic environment. The sequence, timing, and loudness of the different sounds allow the network to be monitored and anomalous behaviour to be detected without the need to continuously watch a monitor screen.

Identifying important nodes by adaptive LeaderRank

NASA Astrophysics Data System (ADS)

Xu, Shuang; Wang, Pei

2017-03-01

Spreading process is a common phenomenon in complex networks. Identifying important nodes in complex networks is of great significance in real-world applications. Based on the spreading process on networks, a lot of measures have been proposed to evaluate the importance of nodes. However, most of the existing measures are appropriate to static networks, which are fragile to topological perturbations. Many real-world complex networks are dynamic rather than static, meaning that the nodes and edges of such networks may change with time, which challenge numerous existing centrality measures. Based on a new weighted mechanism and the newly proposed H-index and LeaderRank (LR), this paper introduces a variant of the LR measure, called adaptive LeaderRank (ALR), which is a new member of the LR-family. Simulations on six real-world networks reveal that the new measure can well balance between prediction accuracy and robustness. More interestingly, the new measure can better adapt to the adjustment or local perturbations of network topologies, as compared with the existing measures. By discussing the detailed properties of the measures from the LR-family, we illustrate that the ALR has its competitive advantages over the other measures. The proposed algorithm enriches the measures to understand complex networks, and may have potential applications in social networks and biological systems.

EPICS as a MARTe Configuration Environment

NASA Astrophysics Data System (ADS)

Valcarcel, Daniel F.; Barbalace, Antonio; Neto, André; Duarte, André S.; Alves, Diogo; Carvalho, Bernardo B.; Carvalho, Pedro J.; Sousa, Jorge; Fernandes, Horácio; Goncalves, Bruno; Sartori, Filippo; Manduchi, Gabriele

2011-08-01

The Multithreaded Application Real-Time executor (MARTe) software provides an environment for the hard real-time execution of codes while leveraging a standardized algorithm development process. The Experimental Physics and Industrial Control System (EPICS) software allows the deployment and remote monitoring of networked control systems. Channel Access (CA) is the protocol that enables the communication between EPICS distributed components. It allows to set and monitor process variables across the network belonging to different systems. The COntrol and Data Acquisition and Communication (CODAC) system for the ITER Tokamak will be EPICS based and will be used to monitor and live configure the plant controllers. The reconfiguration capability in a hard real-time system requires strict latencies from the request to the actuation and it is a key element in the design of the distributed control algorithm. Presently, MARTe and its objects are configured using a well-defined structured language. After each configuration, all objects are destroyed and the system rebuilt, following the strong hard real-time rule that a real-time system in online mode must behave in a strictly deterministic fashion. This paper presents the design and considerations to use MARTe as a plant controller and enable it to be EPICS monitorable and configurable without disturbing the execution at any time, in particular during a plasma discharge. The solutions designed for this will be presented and discussed.

Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network.

PubMed

Islam, Kh Tohidul; Raj, Ram Gopal

2017-04-13

Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are 'traffic light ahead' or 'pedestrian crossing' indications. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real world road signs are then extracted using vision-only information. The system is based on two stages, one performs the detection and another one is for recognition. In the first stage, a hybrid color segmentation algorithm has been developed and tested. In the second stage, an introduced robust custom feature extraction method is used for the first time in a road sign recognition approach. Finally, a multilayer artificial neural network (ANN) has been created to recognize and interpret various road signs. It is robust because it has been tested on both standard and non-standard road signs with significant recognition accuracy. This proposed system achieved an average of 99.90% accuracy with 99.90% of sensitivity, 99.90% of specificity, 99.90% of f-measure, and 0.001 of false positive rate (FPR) with 0.3 s computational time. This low FPR can increase the system stability and dependability in real-time applications.

Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network

PubMed Central

Islam, Kh Tohidul; Raj, Ram Gopal

2017-01-01

Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are ‘traffic light ahead’ or ‘pedestrian crossing’ indications. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real world road signs are then extracted using vision-only information. The system is based on two stages, one performs the detection and another one is for recognition. In the first stage, a hybrid color segmentation algorithm has been developed and tested. In the second stage, an introduced robust custom feature extraction method is used for the first time in a road sign recognition approach. Finally, a multilayer artificial neural network (ANN) has been created to recognize and interpret various road signs. It is robust because it has been tested on both standard and non-standard road signs with significant recognition accuracy. This proposed system achieved an average of 99.90% accuracy with 99.90% of sensitivity, 99.90% of specificity, 99.90% of f-measure, and 0.001 of false positive rate (FPR) with 0.3 s computational time. This low FPR can increase the system stability and dependability in real-time applications. PMID:28406471

Coarse-to-fine deep neural network for fast pedestrian detection

NASA Astrophysics Data System (ADS)

Li, Yaobin; Yang, Xinmei; Cao, Lijun

2017-11-01

Pedestrian detection belongs to a category of object detection is a key issue in the field of video surveillance and automatic driving. Although recent object detection methods, such as Fast/Faster RCNN, have achieved excellent performance, it is difficult to meet real-time requirements and limits the application in real scenarios. A coarse-to-fine deep neural network for fast pedestrian detection is proposed in this paper. Two-stage approach is presented to realize fine trade-off between accuracy and speed. In the coarse stage, we train a fast deep convolution neural network to generate most pedestrian candidates at the cost of a number of false positives. The detector can cover the majority of scales, sizes, and occlusions of pedestrians. After that, a classification network is introduced to refine the pedestrian candidates generated from the previous stage. Refining through classification network, most of false detections will be excluded easily and the final pedestrian predictions with bounding box and confidence score are produced. Competitive results have been achieved on INRIA dataset in terms of accuracy, especially the method can achieve real-time detection that is faster than the previous leading methods. The effectiveness of coarse-to-fine approach to detect pedestrians is verified, and the accuracy and stability are also improved.

Centrality measures in temporal networks with time series analysis

NASA Astrophysics Data System (ADS)

Huang, Qiangjuan; Zhao, Chengli; Zhang, Xue; Wang, Xiaojie; Yi, Dongyun

2017-05-01

The study of identifying important nodes in networks has a wide application in different fields. However, the current researches are mostly based on static or aggregated networks. Recently, the increasing attention to networks with time-varying structure promotes the study of node centrality in temporal networks. In this paper, we define a supra-evolution matrix to depict the temporal network structure. With using of the time series analysis, the relationships between different time layers can be learned automatically. Based on the special form of the supra-evolution matrix, the eigenvector centrality calculating problem is turned into the calculation of eigenvectors of several low-dimensional matrices through iteration, which effectively reduces the computational complexity. Experiments are carried out on two real-world temporal networks, Enron email communication network and DBLP co-authorship network, the results of which show that our method is more efficient at discovering the important nodes than the common aggregating method.

Dynamic Data Driven Applications Systems (DDDAS)

DTIC Science & Technology

2013-03-06

INS •  Chip-scale atomic clocks •  Ad hoc networks •  Polymorphic networks •  Agile networks •  Laser communications •  Frequency-agile RF...atomi clocks •  Ad hoc networks •  Polymorphic networks •  Agile networks •  Laser co munications •  Frequency-agile RF systems...Real-Time Doppler Wind Wind field Sensor observations Energy Estimation Atmospheric Models for On-line Planning Planning and Control

Network Coded Cooperative Communication in a Real-Time Wireless Hospital Sensor Network.

PubMed

Prakash, R; Balaji Ganesh, A; Sivabalan, Somu

2017-05-01

The paper presents a network coded cooperative communication (NC-CC) enabled wireless hospital sensor network architecture for monitoring health as well as postural activities of a patient. A wearable device, referred as a smartband is interfaced with pulse rate, body temperature sensors and an accelerometer along with wireless protocol services, such as Bluetooth and Radio-Frequency transceiver and Wi-Fi. The energy efficiency of wearable device is improved by embedding a linear acceleration based transmission duty cycling algorithm (NC-DRDC). The real-time demonstration is carried-out in a hospital environment to evaluate the performance characteristics, such as power spectral density, energy consumption, signal to noise ratio, packet delivery ratio and transmission offset. The resource sharing and energy efficiency features of network coding technique are improved by proposing an algorithm referred as network coding based dynamic retransmit/rebroadcast decision control (LA-TDC). From the experimental results, it is observed that the proposed LA-TDC algorithm reduces network traffic and end-to-end delay by an average of 27.8% and 21.6%, respectively than traditional network coded wireless transmission. The wireless architecture is deployed in a hospital environment and results are then successfully validated.

American River Hydrologic Observatory

NASA Astrophysics Data System (ADS)

Glaser, S. D.; Bales, R. C.; Conklin, M. H.

2016-12-01

We have set up fourteen large wireless sensor networks to measure hydrologic parameters over physiographical representative regions of the snow-dominated portion of the river basin. This is perhaps the largest wireless sensor network in the world. Each network covers about a 1 km2 area and consists of about 45 elements. We measure snow depth, temperature humidity soil moisture and temperature, and solar radiation in real time at ten locations per site, as opposed to the traditional once-a-month snow course. As part of the multi-PI SSCZO, we have installed a 62-node wireless sensor network to measure snow depth, temperature humidity soil moisture and temperature, and solar radiation in real time. This network has been operating for approximately six years. We are now installing four large wireless sensor networks to measure snow depth, temperature humidity soil moisture and temperature, and solar radiation in East Branch of the North Fork of the Feather River, CA. The presentation will discuss the planning and operation of the networks as well as some unique results. It will also present information about the networking hardware designed for these installations, which has resulted in a start-up, Metronome Systems.

Physical layer one-time-pad data encryption through synchronized semiconductor laser networks

NASA Astrophysics Data System (ADS)

Argyris, Apostolos; Pikasis, Evangelos; Syvridis, Dimitris

2016-02-01

Semiconductor lasers (SL) have been proven to be a key device in the generation of ultrafast true random bit streams. Their potential to emit chaotic signals under conditions with desirable statistics, establish them as a low cost solution to cover various needs, from large volume key generation to real-time encrypted communications. Usually, only undemanding post-processing is needed to convert the acquired analog timeseries to digital sequences that pass all established tests of randomness. A novel architecture that can generate and exploit these true random sequences is through a fiber network in which the nodes are semiconductor lasers that are coupled and synchronized to central hub laser. In this work we show experimentally that laser nodes in such a star network topology can synchronize with each other through complex broadband signals that are the seed to true random bit sequences (TRBS) generated at several Gb/s. The potential for each node to access real-time generated and synchronized with the rest of the nodes random bit streams, through the fiber optic network, allows to implement an one-time-pad encryption protocol that mixes the synchronized true random bit sequence with real data at Gb/s rates. Forward-error correction methods are used to reduce the errors in the TRBS and the final error rate at the data decoding level. An appropriate selection in the sampling methodology and properties, as well as in the physical properties of the chaotic seed signal through which network locks in synchronization, allows an error free performance.

Towards a Community Environmental Observation Network

NASA Astrophysics Data System (ADS)

Mertl, Stefan; Lettenbichler, Anton

2014-05-01

The Community Environmental Observation Network (CEON) is dedicated to the development of a free sensor network to collect and distribute environmental data (e.g. ground shaking, climate parameters). The data collection will be done with contributions from citizens, research institutions and public authorities like communities or schools. This will lead to a large freely available data base which can be used for public information, research, the arts,..... To start a free sensor network, the most important step is to provide easy access to free data collection and -distribution tools. The initial aims of the project CEON are dedicated to the development of these tools. A high quality data logger based on open hardware and free software is developed and a software suite of already existing free software for near-real time data communication and data distribution over the Internet will be assembled. Foremost, the development focuses on the collection of data related to the deformation of the earth (such as ground shaking, surface displacement of mass movements and glaciers) and the collection of climate data. The extent to other measurements will be considered in the design. The data logger is built using open hardware prototyping platforms like BeagleBone Black and Arduino. Main features of the data logger are: a 24Bit analog-to-digital converter; a GPS module for time reference and positioning; wireless mesh networking using Optimized Link State Routing; near real-time data transmission and communication; and near real-time differential GNSS positioning using the RTKLIB software. The project CEON is supported by the Internet Foundation Austria (IPA) within the NetIdee 2013 call.

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.

Lab Streaming Layer Enabled Myo Data Collection Software User Manual

DTIC Science & Technology

2017-06-07

time - series data over a local network. LSL handles the networking, time -synchronization, (near-) real- time access as well as, optionally, the... series data collection (e.g., brain activity, heart activity, muscle activity) using the LSL application programming interface (API). Time -synchronized...saved to a single extensible data format (XDF) file. Once the time - series data are collected in a Lab Recorder XDF file, users will be able to query

Nationwide online social networking for cardiovascular care in Korea using Facebook.

PubMed

Kim, Changsun; Kang, Bo Seung; Choi, Hyuk Joong; Lee, Young Joo; Kang, Gu Hyun; Choi, Wook Jin; Kwon, In Ho

2014-01-01

To examine the use of online social networking for cardiovascular care using Facebook. All posts and comments in a Facebook group between June 2011 and May 2012 were reviewed, and a survey was conducted. A total of 298 members participated. Of the 277 wall posts, 26.7% were question posts requesting rapid replies, and 50.5% were interesting cases shared with other members. The median response time for the question posts was 16 min (IQR 8-47), which tended to decrease as more members joined the group. Many members (37.4%) accessed the group more than once a day, and more than half (64%) monitored the group posts in real time with automatic notifications of new posts. Most members expressed confidence in the content posted. Facebook enables online social networking between physicians in near-real time and appears to be a useful tool for physicians to share clinical experience and request assistance in decision-making.

A neuromorphic network for generic multivariate data classification

PubMed Central

Schmuker, Michael; Pfeil, Thomas; Nawrot, Martin Paul

2014-01-01

Computational neuroscience has uncovered a number of computational principles used by nervous systems. At the same time, neuromorphic hardware has matured to a state where fast silicon implementations of complex neural networks have become feasible. En route to future technical applications of neuromorphic computing the current challenge lies in the identification and implementation of functional brain algorithms. Taking inspiration from the olfactory system of insects, we constructed a spiking neural network for the classification of multivariate data, a common problem in signal and data analysis. In this model, real-valued multivariate data are converted into spike trains using “virtual receptors” (VRs). Their output is processed by lateral inhibition and drives a winner-take-all circuit that supports supervised learning. VRs are conveniently implemented in software, whereas the lateral inhibition and classification stages run on accelerated neuromorphic hardware. When trained and tested on real-world datasets, we find that the classification performance is on par with a naïve Bayes classifier. An analysis of the network dynamics shows that stable decisions in output neuron populations are reached within less than 100 ms of biological time, matching the time-to-decision reported for the insect nervous system. Through leveraging a population code, the network tolerates the variability of neuronal transfer functions and trial-to-trial variation that is inevitably present on the hardware system. Our work provides a proof of principle for the successful implementation of a functional spiking neural network on a configurable neuromorphic hardware system that can readily be applied to real-world computing problems. PMID:24469794

Real-Time Multimission Event Notification System for Mars Relay

NASA Technical Reports Server (NTRS)

Wallick, Michael N.; Allard, Daniel A.; Gladden, Roy E.; Wang, Paul; Hy, Franklin H.

2013-01-01

As the Mars Relay Network is in constant flux (missions and teams going through their daily workflow), it is imperative that users are aware of such state changes. For example, a change by an orbiter team can affect operations on a lander team. This software provides an ambient view of the real-time status of the Mars network. The Mars Relay Operations Service (MaROS) comprises a number of tools to coordinate, plan, and visualize various aspects of the Mars Relay Network. As part of MaROS, a feature set was developed that operates on several levels of the software architecture. These levels include a Web-based user interface, a back-end "ReSTlet" built in Java, and databases that store the data as it is received from the network. The result is a real-time event notification and management system, so mission teams can track and act upon events on a moment-by-moment basis. This software retrieves events from MaROS and displays them to the end user. Updates happen in real time, i.e., messages are pushed to the user while logged into the system, and queued when the user is not online for later viewing. The software does not do away with the email notifications, but augments them with in-line notifications. Further, this software expands the events that can generate a notification, and allows user-generated notifications. Existing software sends a smaller subset of mission-generated notifications via email. A common complaint of users was that the system-generated e-mails often "get lost" with other e-mail that comes in. This software allows for an expanded set (including user-generated) of notifications displayed in-line of the program. By separating notifications, this can improve a user's workflow.

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

Real-time network traffic classification technique for wireless local area networks based on compressed sensing

NASA Astrophysics Data System (ADS)

Balouchestani, Mohammadreza

2017-05-01

Network traffic or data traffic in a Wireless Local Area Network (WLAN) is the amount of network packets moving across a wireless network from each wireless node to another wireless node, which provide the load of sampling in a wireless network. WLAN's Network traffic is the main component for network traffic measurement, network traffic control and simulation. Traffic classification technique is an essential tool for improving the Quality of Service (QoS) in different wireless networks in the complex applications such as local area networks, wireless local area networks, wireless personal area networks, wireless metropolitan area networks, and wide area networks. Network traffic classification is also an essential component in the products for QoS control in different wireless network systems and applications. Classifying network traffic in a WLAN allows to see what kinds of traffic we have in each part of the network, organize the various kinds of network traffic in each path into different classes in each path, and generate network traffic matrix in order to Identify and organize network traffic which is an important key for improving the QoS feature. To achieve effective network traffic classification, Real-time Network Traffic Classification (RNTC) algorithm for WLANs based on Compressed Sensing (CS) is presented in this paper. The fundamental goal of this algorithm is to solve difficult wireless network management problems. The proposed architecture allows reducing False Detection Rate (FDR) to 25% and Packet Delay (PD) to 15 %. The proposed architecture is also increased 10 % accuracy of wireless transmission, which provides a good background for establishing high quality wireless local area networks.

User’s manual for the Automated Data Assurance and Management application developed for quality control of Everglades Depth Estimation Network water-level data

USGS Publications Warehouse

Petkewich, Matthew D.; Daamen, Ruby C.; Roehl, Edwin A.; Conrads, Paul

2016-09-29

The generation of Everglades Depth Estimation Network (EDEN) daily water-level and water-depth maps is dependent on high quality real-time data from over 240 water-level stations. To increase the accuracy of the daily water-surface maps, the Automated Data Assurance and Management (ADAM) tool was created by the U.S. Geological Survey as part of Greater Everglades Priority Ecosystems Science. The ADAM tool is used to provide accurate quality-assurance review of the real-time data from the EDEN network and allows estimation or replacement of missing or erroneous data. This user’s manual describes how to install and operate the ADAM software. File structure and operation of the ADAM software is explained using examples.

The Virtual Earth-Solar Observatory of the SCiESMEX

NASA Astrophysics Data System (ADS)

De la Luz, V.; Gonzalez-Esparza, A.; Cifuentes-Nava, G.

2015-12-01

The Mexican Space Weather Service (SCiESMEX, http://www.sciesmex.unam.mx) started operations in October 2014. The project includes the Virtual Earth-Solar Observatory (VESO, http://www.veso.unam.mx). The VESO is a improved project wich objetive is integrate the space weather instrumentation network from the National Autonomous University of Mexico (UNAM). The network includes the Mexican Array Radiotelescope (MEXART), the Callisto receptor (MEXART), a Neutron Telescope, a Cosmic Ray Telescope. the Schumann Antenna, the National Magnetic Service, and the mexican GPS network (TlalocNet). The VESO facility is located at the Geophysics Institute campus Michoacan (UNAM). We offer the service of data store, real-time data, and quasi real-time data. The hardware of VESO includes a High Performance Computer (HPC) dedicated specially to big data storage.

Classification of Respiratory Sounds by Using An Artificial Neural Network

DTIC Science & Technology

2001-10-28

CLASSIFICATION OF RESPIRATORY SOUNDS BY USING AN ARTIFICIAL NEURAL NETWORK M.C. Sezgin, Z. Dokur, T. Ölmez, M. Korürek Department of Electronics and...successfully classified by the GAL network. Keywords-Respiratory Sounds, Classification of Biomedical Signals, Artificial Neural Network . I. INTRODUCTION...process, feature extraction, and classification by the artificial neural network . At first, the RS signal obtained from a real-time measurement equipment is

Integrated Meteorological Observation Network in Castile-León (Spain)

NASA Astrophysics Data System (ADS)

Merino, A.; Guerrero-Higueras, A. M.; Ortiz de Galisteo, J. P.; López, L.; García-Ortega, E.; Nafría, D. A.; Sánchez, J. L.

2012-04-01

In the region of Castile-Leon, in the northwest of Spain, the study of weather risks is extremely complex because of the topography, the large land area of the region and the variety of climatic features involved. Therefore, as far as the calibration and validation of the necessary tools for the identification and nowcasting of these risks are concerned, one of the most important difficulties is the lack of observed data. The same problem arises, for example, in the analysis of particularly relevant case studies. It was hence deemed necessary to create an INTEGRATED METEOROLOGICAL OBSERVATION NETWORK FOR CASTILE-LEON. The aim of this network is to integrate within one single platform all the ground truth data available. These data enable us to detect a number of weather risks in real time. The various data sources should include the networks from the weather stations run by different public institutions - national and regional ones (AEMET, Junta de Castilla y León, Universities, etc.) -, as well as the stations run by voluntary observers. The platform will contain real or cuasi-real time data from the ground weather stations, but it will also have applications to enable voluntary observers to indicate the presence or absence of certain meteors (snow, hail) or even provide detailed information about them (hailstone size, graupel, etc.). The data managed by this network have a high scientific potential, as they may be used for a number of different purposes: calibration and validation of remote sensing tools, assimilation of observation data from numerical models, study of extreme weather events, etc. An additional aim of the network is the drawing of maps of weather risks in real time. These maps are of great importance for the people involved in risk management in each region, as well as for the general public. Finally, one of the first applications developed has been the creation of observation maps in real time. These applications have been constructed using NCL (NCAR Command Language), because it is a robust tool especially designed for the treatment and visualization of scientific data. Acknowledgements The authors would like to thank the Regional Government of Castile-León for its financial support through the project LE220A11-2.

75 FR 57307 - Consolidated Tape Association; Notice of Filing and Immediate Effectiveness of the Fourteenth...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-20

... to a vendor's dissemination of a real-time Network B last sale price information ticker over...: BATS Exchange, Inc.; Chicago Board Options Exchange, Inc.; Chicago Stock Exchange, Inc.; Financial.... Network B Television Ticker Fees The amendment seeks to establish as a permanent part of the Network B...

Bluetooth-Assisted Context-Awareness in Educational Data Networks

ERIC Educational Resources Information Center

Gonzalez-Castano, F. J.; Garcia-Reinoso, J.; Gil-Castineira, F.; Costa-Montenegro, E.; Pousada-Carballo, J. M.

2005-01-01

In this paper, we propose an auxiliary "location network", to support user-independent context-awareness in educational data networks; for example, to help visitors in a museum. We assume that, in such scenarios, there exist "service servers" that need to be aware of user location in real-time. Specifically, we propose the implementation of a…

A Model for Field Deployment of Wireless Sensor Networks (WSNs) within the Domain of Microclimate Habitat Monitoring

ERIC Educational Resources Information Center

Sanborn, Mark

2011-01-01

Wireless sensor networks (WSNs) represent a class of miniaturized information systems designed to monitor physical environments. These smart monitoring systems form collaborative networks utilizing autonomous sensing, data-collection, and processing to provide real-time analytics of observed environments. As a fundamental research area in…

Independent component analysis (ICA) and self-organizing map (SOM) approach to multidetection system for network intruders

NASA Astrophysics Data System (ADS)

Abdi, Abdi M.; Szu, Harold H.

2003-04-01

With the growing rate of interconnection among computer systems, network security is becoming a real challenge. Intrusion Detection System (IDS) is designed to protect the availability, confidentiality and integrity of critical network information systems. Today"s approach to network intrusion detection involves the use of rule-based expert systems to identify an indication of known attack or anomalies. However, these techniques are less successful in identifying today"s attacks. Hackers are perpetually inventing new and previously unanticipated techniques to compromise information infrastructure. This paper proposes a dynamic way of detecting network intruders on time serious data. The proposed approach consists of a two-step process. Firstly, obtaining an efficient multi-user detection method, employing the recently introduced complexity minimization approach as a generalization of a standard ICA. Secondly, we identified unsupervised learning neural network architecture based on Kohonen"s Self-Organizing Map for potential functional clustering. These two steps working together adaptively will provide a pseudo-real time novelty detection attribute to supplement the current intrusion detection statistical methodology.

CÆLIS: software for assimilation, management and processing data of an atmospheric measurement network

NASA Astrophysics Data System (ADS)

Fuertes, David; Toledano, Carlos; González, Ramiro; Berjón, Alberto; Torres, Benjamín; Cachorro, Victoria E.; de Frutos, Ángel M.

2018-02-01

Given the importance of the atmospheric aerosol, the number of instruments and measurement networks which focus on its characterization are growing. Many challenges are derived from standardization of protocols, monitoring of the instrument status to evaluate the network data quality and manipulation and distribution of large volume of data (raw and processed). CÆLIS is a software system which aims at simplifying the management of a network, providing tools by monitoring the instruments, processing the data in real time and offering the scientific community a new tool to work with the data. Since 2008 CÆLIS has been successfully applied to the photometer calibration facility managed by the University of Valladolid, Spain, in the framework of Aerosol Robotic Network (AERONET). Thanks to the use of advanced tools, this facility has been able to analyze a growing number of stations and data in real time, which greatly benefits the network management and data quality control. The present work describes the system architecture of CÆLIS and some examples of applications and data processing.

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.

Adaptive neural network/expert system that learns fault diagnosis for different structures

NASA Astrophysics Data System (ADS)

Simon, Solomon H.

1992-08-01

Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

Applying Web-Based Tools for Research, Engineering, and Operations

NASA Technical Reports Server (NTRS)

Ivancic, William D.

2011-01-01

Personnel in the NASA Glenn Research Center Network and Architectures branch have performed a variety of research related to space-based sensor webs, network centric operations, security and delay tolerant networking (DTN). Quality documentation and communications, real-time monitoring and information dissemination are critical in order to perform quality research while maintaining low cost and utilizing multiple remote systems. This has been accomplished using a variety of Internet technologies often operating simultaneously. This paper describes important features of various technologies and provides a number of real-world examples of how combining Internet technologies can enable a virtual team to act efficiently as one unit to perform advanced research in operational systems. Finally, real and potential abuses of power and manipulation of information and information access is addressed.

OGS improvements in 2012 in running the Northeastern Italy Seismic Network: the Ferrara VBB borehole seismic station

NASA Astrophysics Data System (ADS)

Pesaresi, Damiano; Romanelli, Marco; Barnaba, Carla; Bragato, Pier Luigi; Durì, Giorgio

2013-04-01

The Centro di Ricerche Sismologiche (CRS, Seismological Research Center) of the Istituto Nazionale di Oceanografia e di Geofisica Sperimentale (OGS, Italian National Institute for Oceanography and Experimental Geophysics) in Udine (Italy) after the strong earthquake of magnitude M=6.4 occurred in 1976 in the Italian Friuli-Venezia Giulia region, started to operate the Northeastern Italy Seismic Network: it currently consists of 17 very sensitive broad band and 18 simpler short period seismic stations, all telemetered to and acquired in real time at the OGS-CRS data center in Udine. Real time data exchange agreements in place with other Italian, Slovenian, Austrian and Swiss seismological institutes lead to a total number of about 100 seismic stations acquired in real time, which makes the OGS the reference institute for seismic monitoring of Northeastern Italy. The southwestern edge of the OGS seismic network stands on the Po alluvial basin: earthquake localization and characterization in this area is affected by the presence of soft alluvial deposits. OGS ha already experience in running a local seismic network in high noise conditions making use of borehole installations in the case of the micro-seismicity monitoring of a local gas storage site for a private company. Following the ML=5.9 earthquake that struck the Emilia region around Ferrara in Northern Italy on May 20, 2012 at 02:03:53 UTC, a cooperation of Istituto Nazionale di Geofisica e Vulcanologia, OGS, the Comune di Ferrara and the University of Ferrara lead to the reinstallation of a previously existing very broad band (VBB) borehole seismic station in Ferrara. The aim of the OGS intervention was on one hand to extend its real time seismic monitoring capabilities toward South-West, including Ferrara and its surroundings, and on the other hand to evaluate the seismic response at the site. We will describe improvements in running the Northeastern Italy Seismic Network, including details of the Ferrara VBB borehole station configuration and installation, with first results.

A Datacenter Backstage: The Knowledge that Supports the Brazilian Seismic Network

NASA Astrophysics Data System (ADS)

Calhau, J.; Assumpcao, M.; Collaço, B.; Bianchi, M.; Pirchiner, M.

2015-12-01

Historically, Brazilian seismology never had a clear strategic vision about how its data should be acquired, evaluated, stored and shared. Without a data management plan, data (for any practical purpose) could be lost, resulting in a non-uniform coverage that will reduce any chance of local and international collaboration, i.e., data will never become scientific knowledge. Since 2009, huge efforts from four different institutions are establishing the new permanent Brazilian Seismographic Network (RSBR), mainly with resources from PETROBRAS, the Brazilian Government oil company. Four FDSN sub-networks currently compose RSBR, with a total of 80 permanent stations. BL and BR codes (from BRASIS subnet) with 47 stations maintained by University of Sao Paulo (USP) and University of Brasilia (UnB) respectively; NB code (RSISNE subnet), with 16 stations deployed by University of Rio Grande do Norte (UFRN); and ON code (RSIS subnet), with 18 stations operated by the National Observatory (ON) in Rio de Janeiro. Most stations transmit data in real-time via satellite or cell-phone links. Each node acquires its own stations locally, and data is real-time shared using SeedLink. Archived data is distributed via ArcLink and/or FDSNWS services. All nodes use the SeisComP3 system for real-time processing and as a levering back-end. Open-source solutions like Seiscomp3 require some homemade tools to be developed, to help solve the most common daily problems of a data management center: local magnitude into the real-time earthquake processor, website plugins, regional earthquake catalog, contribution with ISC catalog, quality-control tools, data request tools, etc. The main data products and community activities include: kml files, data availability plots, request charts, summer school courses, an Open Lab Day and news interviews. Finally, a good effort was made to establish BRASIS sub-network and the whole RSBR as a unified project, that serves as a communication channel between individuals operating local networks.

Real-time parallel processing of grammatical structure in the fronto-striatal system: a recurrent network simulation study using reservoir computing.

PubMed

Hinaut, Xavier; Dominey, Peter Ford

2013-01-01

Sentence processing takes place in real-time. Previous words in the sentence can influence the processing of the current word in the timescale of hundreds of milliseconds. Recent neurophysiological studies in humans suggest that the fronto-striatal system (frontal cortex, and striatum--the major input locus of the basal ganglia) plays a crucial role in this process. The current research provides a possible explanation of how certain aspects of this real-time processing can occur, based on the dynamics of recurrent cortical networks, and plasticity in the cortico-striatal system. We simulate prefrontal area BA47 as a recurrent network that receives on-line input about word categories during sentence processing, with plastic connections between cortex and striatum. We exploit the homology between the cortico-striatal system and reservoir computing, where recurrent frontal cortical networks are the reservoir, and plastic cortico-striatal synapses are the readout. The system is trained on sentence-meaning pairs, where meaning is coded as activation in the striatum corresponding to the roles that different nouns and verbs play in the sentences. The model learns an extended set of grammatical constructions, and demonstrates the ability to generalize to novel constructions. It demonstrates how early in the sentence, a parallel set of predictions are made concerning the meaning, which are then confirmed or updated as the processing of the input sentence proceeds. It demonstrates how on-line responses to words are influenced by previous words in the sentence, and by previous sentences in the discourse, providing new insight into the neurophysiology of the P600 ERP scalp response to grammatical complexity. This demonstrates that a recurrent neural network can decode grammatical structure from sentences in real-time in order to generate a predictive representation of the meaning of the sentences. This can provide insight into the underlying mechanisms of human cortico-striatal function in sentence processing.

Real-Time Parallel Processing of Grammatical Structure in the Fronto-Striatal System: A Recurrent Network Simulation Study Using Reservoir Computing

PubMed Central

Hinaut, Xavier; Dominey, Peter Ford

2013-01-01

Sentence processing takes place in real-time. Previous words in the sentence can influence the processing of the current word in the timescale of hundreds of milliseconds. Recent neurophysiological studies in humans suggest that the fronto-striatal system (frontal cortex, and striatum – the major input locus of the basal ganglia) plays a crucial role in this process. The current research provides a possible explanation of how certain aspects of this real-time processing can occur, based on the dynamics of recurrent cortical networks, and plasticity in the cortico-striatal system. We simulate prefrontal area BA47 as a recurrent network that receives on-line input about word categories during sentence processing, with plastic connections between cortex and striatum. We exploit the homology between the cortico-striatal system and reservoir computing, where recurrent frontal cortical networks are the reservoir, and plastic cortico-striatal synapses are the readout. The system is trained on sentence-meaning pairs, where meaning is coded as activation in the striatum corresponding to the roles that different nouns and verbs play in the sentences. The model learns an extended set of grammatical constructions, and demonstrates the ability to generalize to novel constructions. It demonstrates how early in the sentence, a parallel set of predictions are made concerning the meaning, which are then confirmed or updated as the processing of the input sentence proceeds. It demonstrates how on-line responses to words are influenced by previous words in the sentence, and by previous sentences in the discourse, providing new insight into the neurophysiology of the P600 ERP scalp response to grammatical complexity. This demonstrates that a recurrent neural network can decode grammatical structure from sentences in real-time in order to generate a predictive representation of the meaning of the sentences. This can provide insight into the underlying mechanisms of human cortico-striatal function in sentence processing. PMID:23383296

Towards an Earthquake and Tsunami Early Warning in the Caribbean

NASA Astrophysics Data System (ADS)

Huerfano Moreno, V. A.; Vanacore, E. A.

2017-12-01

The Caribbean region (CR) has a documented history of large damaging earthquakes and tsunamis that have affected coastal areas, including the events of Jamaica in 1692, Virgin Islands in 1867, Puerto Rico in 1918, the Dominican Republic in 1946 and Haiti in 2010. There is clear evidence that tsunamis have been triggered by large earthquakes that deformed the ocean floor around the Caribbean Plate boundary. The CR is monitored jointly by national/regional/local seismic, geodetic and sea level networks. All monitoring institutions are participating in the UNESCO ICG/Caribe EWS, the purpose of this initiative is to minimize loss of life and destruction of property, and to mitigate against catastrophic economic impacts via promoting local research, real time (RT) earthquake, geodetic and sea level data sharing and improving warning capabilities and enhancing education and outreach strategies. Currently more than, 100 broad-band seismic, 65 sea levels and 50 GPS high rate stations are available in real or near real-time. These real-time streams are used by Local/Regional or Worldwide detection and warning institutions to provide earthquake source parameters in a timely manner. Currently, any Caribbean event detected to have a magnitude greater than 4.5 is evaluated, and sea level is measured, by the TWC for tsumanigenic potential. The regional cooperation is motivated both by research interests as well as geodetic, seismic and tsunami hazard monitoring and warning. It will allow the imaging of the tectonic structure of the Caribbean region to a high resolution which will consequently permit further understanding of the seismic source properties for moderate and large events and the application of this knowledge to procedures of civil protection. To reach its goals, the virtual network has been designed following the highest technical standards: BB sensors, 24 bits A/D converters with 140 dB dynamic range, real-time telemetry. Here we will discuss the state of the PR component of this virtual network as well as current advances in the imaging of the PR tectonic structure. The goal of this presentation is to describe the Puerto Rico Seismic Network (PRSN) system, including the real time earthquake and tsunami monitoring as well as the specific protocols used to broadcast earthquake/tsunami messages locally.

The UNAVCO Real-time GPS Data Processing System and Community Reference Data Sets

NASA Astrophysics Data System (ADS)

Sievers, C.; Mencin, D.; Berglund, H. T.; Blume, F.; Meertens, C. M.; Mattioli, G. S.

2013-12-01

UNAVCO has constructed a real-time GPS (RT-GPS) network of 420 GPS stations. The majority of the streaming stations come from the EarthScope Plate Boundary Observatory (PBO) through an NSF-ARRA funded Cascadia Upgrade Initiative that upgraded 100 backbone stations throughout the PBO footprint and 282 stations focused in the Pacific Northwest. Additional contributions from NOAA (~30 stations in Southern California) and the USGS (8 stations at Yellowstone) account for the other real-time stations. Based on community based outcomes of a workshop focused on real-time GPS position data products and formats hosted by UNAVCO in Spring of 2011, UNAVCO now provides real-time PPP positions for all 420 stations using Trimble's PIVOT software and for 50 stations using TrackRT at the volcanic centers located at Yellowstone (Figure 1 shows an example ensemble of TrackRT networks used in processing the Yellowstone data), Mt St Helens, and Montserrat. The UNAVCO real-time system has the potential to enhance our understanding of earthquakes, seismic wave propagation, volcanic eruptions, magmatic intrusions, movement of ice, landslides, and the dynamics of the atmosphere. Beyond its increasing uses for science and engineering, RT-GPS has the potential to provide early warning of hazards to emergency managers, utilities, other infrastructure managers, first responders and others. With the goal of characterizing stability and improving software and higher level products based on real-time GPS time series, UNAVCO is developing an open community standard data set where data processors can provide solutions based on common sets of RT-GPS data which simulate real world scenarios and events. UNAVCO is generating standard data sets for playback that include not only real and synthetic events but also background noise, antenna movement (e.g., steps, linear trends, sine waves, and realistic earthquake-like motions), receiver drop out and online return, interruption of communications (such as, bulk regional failures due to specific carriers during an actual event), satellites rising and setting, various constellation outages and differences in performance between real-time and simulated (retroactive) real-time. We present an overview of the UNAVCO RT-GPS system, a comparison of the UNAVCO generated real-time data products, and an overview of available common data sets.

Real-time software-based end-to-end wireless visual communications simulation platform

NASA Astrophysics Data System (ADS)

Chen, Ting-Chung; Chang, Li-Fung; Wong, Andria H.; Sun, Ming-Ting; Hsing, T. Russell

1995-04-01

Wireless channel impairments pose many challenges to real-time visual communications. In this paper, we describe a real-time software based wireless visual communications simulation platform which can be used for performance evaluation in real-time. This simulation platform consists of two personal computers serving as hosts. Major components of each PC host include a real-time programmable video code, a wireless channel simulator, and a network interface for data transport between the two hosts. The three major components are interfaced in real-time to show the interaction of various wireless channels and video coding algorithms. The programmable features in the above components allow users to do performance evaluation of user-controlled wireless channel effects without physically carrying out these experiments which are limited in scope, time-consuming, and costly. Using this simulation platform as a testbed, we have experimented with several wireless channel effects including Rayleigh fading, antenna diversity, channel filtering, symbol timing, modulation, and packet loss.

Using Smartphones to Detect Earthquakes

NASA Astrophysics Data System (ADS)

Kong, Q.; Allen, R. M.

2012-12-01

We are using the accelerometers in smartphones to record earthquakes. In the future, these smartphones may work as a supplement network to the current traditional network for scientific research and real-time applications. Given the potential number of smartphones, and small separation of sensors, this new type of seismic dataset has significant potential provides that the signal can be separated from the noise. We developed an application for android phones to record the acceleration in real time. These records can be saved on the local phone or transmitted back to a server in real time. The accelerometers in the phones were evaluated by comparing performance with a high quality accelerometer while located on controlled shake tables for a variety of tests. The results show that the accelerometer in the smartphone can reproduce the characteristic of the shaking very well, even the phone left freely on the shake table. The nature of these datasets is also quite different from traditional networks due to the fact that smartphones are moving around with their owners. Therefore, we must distinguish earthquake signals from other daily use. In addition to the shake table tests that accumulated earthquake records, we also recorded different human activities such as running, walking, driving etc. An artificial neural network based approach was developed to distinguish these different records. It shows a 99.7% successful rate of distinguishing earthquakes from the other typical human activities in our database. We are now at the stage ready to develop the basic infrastructure for a smartphone seismic network.

Effective image differencing with convolutional neural networks for real-time transient hunting

NASA Astrophysics Data System (ADS)

Sedaghat, Nima; Mahabal, Ashish

2018-06-01

Large sky surveys are increasingly relying on image subtraction pipelines for real-time (and archival) transient detection. In this process one has to contend with varying point-spread function (PSF) and small brightness variations in many sources, as well as artefacts resulting from saturated stars and, in general, matching errors. Very often the differencing is done with a reference image that is deeper than individual images and the attendant difference in noise characteristics can also lead to artefacts. We present here a deep-learning approach to transient detection that encapsulates all the steps of a traditional image-subtraction pipeline - image registration, background subtraction, noise removal, PSF matching and subtraction - in a single real-time convolutional network. Once trained, the method works lightening-fast and, given that it performs multiple steps in one go, the time saved and false positives eliminated for multi-CCD surveys like Zwicky Transient Facility and Large Synoptic Survey Telescope will be immense, as millions of subtractions will be needed per night.

Experimental demonstration of the real-time online fault monitoring technique for chaos-based passive optical networks

NASA Astrophysics Data System (ADS)

Dou, Xinyu; Yin, Hongxi; Yue, Hehe; Jin, Yu; Shen, Jing; Li, Lin

2015-09-01

In this paper, a real-time online fault monitoring technique for chaos-based passive optical networks (PONs) is proposed and experimentally demonstrated. The fault monitoring is performed by the chaotic communication signal. The proof-of-concept experiments are demonstrated for two PON structures, i.e., wavelength-division-multiplexing (WDM) PON and Ethernet PON (EPON), respectively. For WDM PON, two monitoring approaches are investigated, one deploying a chaotic optical time domain reflectometry (OTDR) for each transmitter, and the other using only one tunable chaotic OTDR. The experimental results show that the faults at beyond 20 km from the OLT can be detected and located. The spatial resolution of the tunable chaotic OTDR is an order of magnitude of centimeter. Meanwhile, the monitoring process can operate in parallel with the chaotic optical secure communications. The proposed technique has benefits of real-time, online, precise fault location, and simple realization, which will significantly reduce the cost of operation, administration and maintenance (OAM) of PON.

High-speed railway real-time localization auxiliary method based on deep neural network

NASA Astrophysics Data System (ADS)

Chen, Dongjie; Zhang, Wensheng; Yang, Yang

2017-11-01

High-speed railway intelligent monitoring and management system is composed of schedule integration, geographic information, location services, and data mining technology for integration of time and space data. Assistant localization is a significant submodule of the intelligent monitoring system. In practical application, the general access is to capture the image sequences of the components by using a high-definition camera, digital image processing technique and target detection, tracking and even behavior analysis method. In this paper, we present an end-to-end character recognition method based on a deep CNN network called YOLO-toc for high-speed railway pillar plate number. Different from other deep CNNs, YOLO-toc is an end-to-end multi-target detection framework, furthermore, it exhibits a state-of-art performance on real-time detection with a nearly 50fps achieved on GPU (GTX960). Finally, we realize a real-time but high-accuracy pillar plate number recognition system and integrate natural scene OCR into a dedicated classification YOLO-toc model.

Real-time distributed video coding for 1K-pixel visual sensor networks

NASA Astrophysics Data System (ADS)

Hanca, Jan; Deligiannis, Nikos; Munteanu, Adrian

2016-07-01

Many applications in visual sensor networks (VSNs) demand the low-cost wireless transmission of video data. In this context, distributed video coding (DVC) has proven its potential to achieve state-of-the-art compression performance while maintaining low computational complexity of the encoder. Despite their proven capabilities, current DVC solutions overlook hardware constraints, and this renders them unsuitable for practical implementations. This paper introduces a DVC architecture that offers highly efficient wireless communication in real-world VSNs. The design takes into account the severe computational and memory constraints imposed by practical implementations on low-resolution visual sensors. We study performance-complexity trade-offs for feedback-channel removal, propose learning-based techniques for rate allocation, and investigate various simplifications of side information generation yielding real-time decoding. The proposed system is evaluated against H.264/AVC intra, Motion-JPEG, and our previously designed DVC prototype for low-resolution visual sensors. Extensive experimental results on various data show significant improvements in multiple configurations. The proposed encoder achieves real-time performance on a 1k-pixel visual sensor mote. Real-time decoding is performed on a Raspberry Pi single-board computer or a low-end notebook PC. To the best of our knowledge, the proposed codec is the first practical DVC deployment on low-resolution VSNs.

Real-time science and outreach from the UNOLS fleet via HiSeasNet

NASA Astrophysics Data System (ADS)

Foley, S.; Berger, J.; Orcutt, J. A.; Brice, D.; Coleman, D. F.; Grabowski, E. M.

2010-12-01

The HiSeasNet satellite communications network has ben providing cost-effective, reliable, continuous Internet connectivity to the UNOLS oceanographic research fleet for nearly nine years. During that time, HiSeasNet has supported science and outreach programs with a variety of real-time interactions back to shore including videoconferencing, webcasting, shared whiteboards, and streaming high-definition video feeds. Solutions have varied in scale, cost, and capability. As real-time science and outreach becomes more common, experience with a variety of technologies continues to build, and more opportunities yet to explore.

Testing the global capabilities of the Antelope software suite: fast location and Mb determination of teleseismic events using the ASAIN and GSN seismic networks

NASA Astrophysics Data System (ADS)

Pesaresi, D.; Russi, M.; Plasencia, M.; Cravos, C.

2009-04-01

The Italian National Institute for Oceanography and Experimental Geophysics (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, OGS) is running the Antarctic Seismographic Argentinean Italian Network (ASAIN), made of 5 seismic stations located in the Scotia Sea region in Antarctica and in Argentina: data from these stations are transferred in real time to the OGS headquarters in Trieste (Italy) via satellite links. OGS is also running, in close cooperation with the Friuli-Venezia Giulia Civil Defense, the North East (NI) Italy seismic network, making use of the Antelope commercial software suite from BRTT as the main acquisition system. As a test to check the global capabilities of Antelope, we set up an instance of Antelope acquiring data in real time from both the regional ASAIN seismic network in Antarctica and a subset of the Global Seismic Network (GSN) funded by the Incorporated Research Institution for Seismology (IRIS). The facilities of the IRIS Data Management System, and specifically the IRIS Data Management Center, were used for real time access to waveform required in this study. Preliminary results over 1 month period indicated that about 82% of the earthquakes with magnitude M>5.0 listed in the PDE catalogue of the National Earthquake Information Center (NEIC) of the United States Geological Survey (USGS) were also correctly detected by Antelope, with an average location error of 0.05 degrees and average body wave magnitude Mb estimation error below 0.1. The average time difference between event origin time and the actual time of event determination by Antelope was of about 45': the comparison with 20', the IASPEI91 P-wave travel time for 180 degrees distance, and 25', the estimate of our test system data latency, indicate that Antelope is a serious candidate for regional and global early warning systems. Updated figures calculated over a longer period of time will be presented and discussed.

Self-organization of complex networks as a dynamical system

NASA Astrophysics Data System (ADS)

Aoki, Takaaki; Yawata, Koichiro; Aoyagi, Toshio

2015-01-01

To understand the dynamics of real-world networks, we investigate a mathematical model of the interplay between the dynamics of random walkers on a weighted network and the link weights driven by a resource carried by the walkers. Our numerical studies reveal that, under suitable conditions, the co-evolving dynamics lead to the emergence of stationary power-law distributions of the resource and link weights, while the resource quantity at each node ceaselessly changes with time. We analyze the network organization as a deterministic dynamical system and find that the system exhibits multistability, with numerous fixed points, limit cycles, and chaotic states. The chaotic behavior of the system leads to the continual changes in the microscopic network dynamics in the absence of any external random noises. We conclude that the intrinsic interplay between the states of the nodes and network reformation constitutes a major factor in the vicissitudes of real-world networks.

Self-organization of complex networks as a dynamical system.

PubMed

Aoki, Takaaki; Yawata, Koichiro; Aoyagi, Toshio

2015-01-01

To understand the dynamics of real-world networks, we investigate a mathematical model of the interplay between the dynamics of random walkers on a weighted network and the link weights driven by a resource carried by the walkers. Our numerical studies reveal that, under suitable conditions, the co-evolving dynamics lead to the emergence of stationary power-law distributions of the resource and link weights, while the resource quantity at each node ceaselessly changes with time. We analyze the network organization as a deterministic dynamical system and find that the system exhibits multistability, with numerous fixed points, limit cycles, and chaotic states. The chaotic behavior of the system leads to the continual changes in the microscopic network dynamics in the absence of any external random noises. We conclude that the intrinsic interplay between the states of the nodes and network reformation constitutes a major factor in the vicissitudes of real-world networks.

Efficient implementation of real-time programs under the VAX/VMS operating system

NASA Technical Reports Server (NTRS)

Johnson, S. C.

1985-01-01

Techniques for writing efficient real-time programs under the VAX/VMS oprating system are presented. Basic operations are presented for executing at real-time priority and for avoiding needlless processing delays. A highly efficient technique for accessing physical devices by mapping to the input/output space and accessing the device registrs directly is described. To illustrate the application of the technique, examples are included of different uses of the technique on three devices in the Langley Avionics Integration Research Lab (AIRLAB): the KW11-K dual programmable real-time clock, the Parallel Communications Link (PCL11-B) communication system, and the Datacom Synchronization Network. Timing data are included to demonstrate the performance improvements realized with these applications of the technique.

Modular architecture for robotics and teleoperation

DOEpatents

Anderson, Robert J.

1996-12-03

Systems and methods for modularization and discretization of real-time robot, telerobot and teleoperation systems using passive, network based control laws. Modules consist of network one-ports and two-ports. Wave variables and position information are passed between modules. The behavior of each module is decomposed into uncoupled linear-time-invariant, and coupled, nonlinear memoryless elements and then are separately discretized.

Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11ah

PubMed Central

Tian, Le; Latré, Steven

2017-01-01

IEEE 802.11ah, marketed as Wi-Fi HaLow, extends Wi-Fi to the sub-1 GHz spectrum. Through a number of physical layer (PHY) and media access control (MAC) optimizations, it aims to bring greatly increased range, energy-efficiency, and scalability. This makes 802.11ah the perfect candidate for providing connectivity to Internet of Things (IoT) devices. One of these new features, referred to as the Restricted Access Window (RAW), focuses on improving scalability in highly dense deployments. RAW divides stations into groups and reduces contention and collisions by only allowing channel access to one group at a time. However, the standard does not dictate how to determine the optimal RAW grouping parameters. The optimal parameters depend on the current network conditions, and it has been shown that incorrect configuration severely impacts throughput, latency and energy efficiency. In this paper, we propose a traffic-adaptive RAW optimization algorithm (TAROA) to adapt the RAW parameters in real time based on the current traffic conditions, optimized for sensor networks in which each sensor transmits packets with a certain (predictable) frequency and may change the transmission frequency over time. The TAROA algorithm is executed at each target beacon transmission time (TBTT), and it first estimates the packet transmission interval of each station only based on packet transmission information obtained by access point (AP) during the last beacon interval. Then, TAROA determines the RAW parameters and assigns stations to RAW slots based on this estimated transmission frequency. The simulation results show that, compared to enhanced distributed channel access/distributed coordination function (EDCA/DCF), the TAROA algorithm can highly improve the performance of IEEE 802.11ah dense networks in terms of throughput, especially when hidden nodes exist, although it does not always achieve better latency performance. This paper contributes with a practical approach to optimizing RAW grouping under dynamic traffic in real time, which is a major leap towards applying RAW mechanism in real-life IoT networks. PMID:28677617

Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11ah.

PubMed

Tian, Le; Khorov, Evgeny; Latré, Steven; Famaey, Jeroen

2017-07-04

IEEE 802.11ah, marketed as Wi-Fi HaLow, extends Wi-Fi to the sub-1 GHz spectrum. Through a number of physical layer (PHY) and media access control (MAC) optimizations, it aims to bring greatly increased range, energy-efficiency, and scalability. This makes 802.11ah the perfect candidate for providing connectivity to Internet of Things (IoT) devices. One of these new features, referred to as the Restricted Access Window (RAW), focuses on improving scalability in highly dense deployments. RAW divides stations into groups and reduces contention and collisions by only allowing channel access to one group at a time. However, the standard does not dictate how to determine the optimal RAW grouping parameters. The optimal parameters depend on the current network conditions, and it has been shown that incorrect configuration severely impacts throughput, latency and energy efficiency. In this paper, we propose a traffic-adaptive RAW optimization algorithm (TAROA) to adapt the RAW parameters in real time based on the current traffic conditions, optimized for sensor networks in which each sensor transmits packets with a certain (predictable) frequency and may change the transmission frequency over time. The TAROA algorithm is executed at each target beacon transmission time (TBTT), and it first estimates the packet transmission interval of each station only based on packet transmission information obtained by access point (AP) during the last beacon interval. Then, TAROA determines the RAW parameters and assigns stations to RAW slots based on this estimated transmission frequency. The simulation results show that, compared to enhanced distributed channel access/distributed coordination function (EDCA/DCF), the TAROA algorithm can highly improve the performance of IEEE 802.11ah dense networks in terms of throughput, especially when hidden nodes exist, although it does not always achieve better latency performance. This paper contributes with a practical approach to optimizing RAW grouping under dynamic traffic in real time, which is a major leap towards applying RAW mechanism in real-life IoT networks.

Saving energy for the data collection point in WBAN network

NASA Astrophysics Data System (ADS)

Nguyen-Duc, Toan; Kamioka, Eiji

2017-11-01

Wireless sensor networking (WSN) has been rapidly developed and become essential in various domains including health care systems. Such systems use WSN to collect real-time medical sensed data, aiming at improving the patient safety. For instance, patients suffered from adverse events, i.e., cardiac or respiratory arrests, are monitored so as to prevent them from getting harm. Sensors are placed on, in or near the patients' body to continuously collect sensing data such as the electrocardiograms, blood oxygenation, breathing, and heart rate. In this case, the sensors form a subcategory of WSN called wireless body area network (WBAN). In WBAN, sensing data are sent to one or more data collection points called personal server (PS). The role of PS is important since it forwards sensed data, to a medical server via a Bluetooth/WLAN connection in real time to support storage of information and real-time diagnosis, the device can also issue a notification of an emergency status. Since PS is a battery-based device, when its battery is empty, it will disconnect the sensed medical data with the rest network. To best of our knowledge, very few studies that focus on saving energy for the PS. To this end, this work investigates the trade-off between energy consumption for wireless communication and the amount of sensing data. An energy consumption model for wireless communication has been proposed based on direct measurement using real testbed. According to our findings, it is possible to save energy for the PS by selecting suitable wireless technology to be used based on the amount of data to be transmitted.

Closed-Loop Control of Complex Networks: A Trade-Off between Time and Energy

NASA Astrophysics Data System (ADS)

Sun, Yong-Zheng; Leng, Si-Yang; Lai, Ying-Cheng; Grebogi, Celso; Lin, Wei

2017-11-01

Controlling complex nonlinear networks is largely an unsolved problem at the present. Existing works focus either on open-loop control strategies and their energy consumptions or on closed-loop control schemes with an infinite-time duration. We articulate a finite-time, closed-loop controller with an eye toward the physical and mathematical underpinnings of the trade-off between the control time and energy as well as their dependence on the network parameters and structure. The closed-loop controller is tested on a large number of real systems including stem cell differentiation, food webs, random ecosystems, and spiking neuronal networks. Our results represent a step forward in developing a rigorous and general framework to control nonlinear dynamical networks with a complex topology.

Real-Time Precise Point Positioning (RTPPP) with raw observations and its application in real-time regional ionospheric VTEC modeling

NASA Astrophysics Data System (ADS)

Liu, Teng; Zhang, Baocheng; Yuan, Yunbin; Li, Min

2018-01-01

Precise Point Positioning (PPP) is an absolute positioning technology mainly used in post data processing. With the continuously increasing demand for real-time high-precision applications in positioning, timing, retrieval of atmospheric parameters, etc., Real-Time PPP (RTPPP) and its applications have drawn more and more research attention in recent years. This study focuses on the models, algorithms and ionospheric applications of RTPPP on the basis of raw observations, in which high-precision slant ionospheric delays are estimated among others in real time. For this purpose, a robust processing strategy for multi-station RTPPP with raw observations has been proposed and realized, in which real-time data streams and State-Space-Representative (SSR) satellite orbit and clock corrections are used. With the RTPPP-derived slant ionospheric delays from a regional network, a real-time regional ionospheric Vertical Total Electron Content (VTEC) modeling method is proposed based on Adjusted Spherical Harmonic Functions and a Moving-Window Filter. SSR satellite orbit and clock corrections from different IGS analysis centers are evaluated. Ten globally distributed real-time stations are used to evaluate the positioning performances of the proposed RTPPP algorithms in both static and kinematic modes. RMS values of positioning errors in static/kinematic mode are 5.2/15.5, 4.7/17.4 and 12.8/46.6 mm, for north, east and up components, respectively. Real-time slant ionospheric delays from RTPPP are compared with those from the traditional Carrier-to-Code Leveling (CCL) method, in terms of function model, formal precision and between-receiver differences of short baseline. Results show that slant ionospheric delays from RTPPP are more precise and have a much better convergence performance than those from the CCL method in real-time processing. 30 real-time stations from the Asia-Pacific Reference Frame network are used to model the ionospheric VTECs over Australia in real time, with slant ionospheric delays from both RTPPP and CCL methods for comparison. RMS of the VTEC differences between RTPPP/CCL method and CODE final products is 0.91/1.09 TECU, and RMS of the VTEC differences between RTPPP and CCL methods is 0.67 TECU. Slant Total Electron Contents retrieved from different VTEC models are also validated with epoch-differenced Geometry-Free combinations of dual-frequency phase observations, and mean RMS values are 2.14, 2.33 and 2.07 TECU for RTPPP method, CCL method and CODE final products, respectively. This shows the superiority of RTPPP-derived slant ionospheric delays in real-time ionospheric VTEC modeling.

Online Community Detection for Large Complex Networks

PubMed Central

Pan, Gang; Zhang, Wangsheng; Wu, Zhaohui; Li, Shijian

2014-01-01

Complex networks describe a wide range of systems in nature and society. To understand complex networks, it is crucial to investigate their community structure. In this paper, we develop an online community detection algorithm with linear time complexity for large complex networks. Our algorithm processes a network edge by edge in the order that the network is fed to the algorithm. If a new edge is added, it just updates the existing community structure in constant time, and does not need to re-compute the whole network. Therefore, it can efficiently process large networks in real time. Our algorithm optimizes expected modularity instead of modularity at each step to avoid poor performance. The experiments are carried out using 11 public data sets, and are measured by two criteria, modularity and NMI (Normalized Mutual Information). The results show that our algorithm's running time is less than the commonly used Louvain algorithm while it gives competitive performance. PMID:25061683

Real Time Data Management for Estimating Probabilities of Incidents and Near Misses

NASA Astrophysics Data System (ADS)

Stanitsas, P. D.; Stephanedes, Y. J.

2011-08-01

Advances in real-time data collection, data storage and computational systems have led to development of algorithms for transport administrators and engineers that improve traffic safety and reduce cost of road operations. Despite these advances, problems in effectively integrating real-time data acquisition, processing, modelling and road-use strategies at complex intersections and motorways remain. These are related to increasing system performance in identification, analysis, detection and prediction of traffic state in real time. This research develops dynamic models to estimate the probability of road incidents, such as crashes and conflicts, and incident-prone conditions based on real-time data. The models support integration of anticipatory information and fee-based road use strategies in traveller information and management. Development includes macroscopic/microscopic probabilistic models, neural networks, and vector autoregressions tested via machine vision at EU and US sites.

Real-time Kp predictions from ACE real time solar wind

NASA Astrophysics Data System (ADS)

Detman, Thomas; Joselyn, Joann

1999-06-01

The Advanced Composition Explorer (ACE) spacecraft provides nearly continuous monitoring of solar wind plasma, magnetic fields, and energetic particles from the Sun-Earth L1 Lagrange point upstream of Earth in the solar wind. The Space Environment Center (SEC) in Boulder receives ACE telemetry from a group of international network of tracking stations. One-minute, and 1-hour averages of solar wind speed, density, temperature, and magnetic field components are posted on SEC's World Wide Web page within 3 to 5 minutes after they are measured. The ACE Real Time Solar Wind (RTSW) can be used to provide real-time warnings and short term forecasts of geomagnetic storms based on the (traditional) Kp index. Here, we use historical data to evaluate the performance of the first real-time Kp prediction algorithm to become operational.

Road Network State Estimation Using Random Forest Ensemble Learning

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

Hou, Yi; Edara, Praveen; Chang, Yohan

Network-scale travel time prediction not only enables traffic management centers (TMC) to proactively implement traffic management strategies, but also allows travelers make informed decisions about route choices between various origins and destinations. In this paper, a random forest estimator was proposed to predict travel time in a network. The estimator was trained using two years of historical travel time data for a case study network in St. Louis, Missouri. Both temporal and spatial effects were considered in the modeling process. The random forest models predicted travel times accurately during both congested and uncongested traffic conditions. The computational times for themore » models were low, thus useful for real-time traffic management and traveler information applications.« less

Using inferential sensors for quality control of Everglades Depth Estimation Network water-level data

USGS Publications Warehouse

Petkewich, Matthew D.; Daamen, Ruby C.; Roehl, Edwin A.; Conrads, Paul

2016-09-29

The Everglades Depth Estimation Network (EDEN), with over 240 real-time gaging stations, provides hydrologic data for freshwater and tidal areas of the Everglades. These data are used to generate daily water-level and water-depth maps of the Everglades that are used to assess biotic responses to hydrologic change resulting from the U.S. Army Corps of Engineers Comprehensive Everglades Restoration Plan. The generation of EDEN daily water-level and water-depth maps is dependent on high quality real-time data from water-level stations. Real-time data are automatically checked for outliers by assigning minimum and maximum thresholds for each station. Small errors in the real-time data, such as gradual drift of malfunctioning pressure transducers, are more difficult to immediately identify with visual inspection of time-series plots and may only be identified during on-site inspections of the stations. Correcting these small errors in the data often is time consuming and water-level data may not be finalized for several months. To provide daily water-level and water-depth maps on a near real-time basis, EDEN needed an automated process to identify errors in water-level data and to provide estimates for missing or erroneous water-level data.The Automated Data Assurance and Management (ADAM) software uses inferential sensor technology often used in industrial applications. Rather than installing a redundant sensor to measure a process, such as an additional water-level station, inferential sensors, or virtual sensors, were developed for each station that make accurate estimates of the process measured by the hard sensor (water-level gaging station). The inferential sensors in the ADAM software are empirical models that use inputs from one or more proximal stations. The advantage of ADAM is that it provides a redundant signal to the sensor in the field without the environmental threats associated with field conditions at stations (flood or hurricane, for example). In the event that a station does malfunction, ADAM provides an accurate estimate for the period of missing data. The ADAM software also is used in the quality assurance and quality control of the data. The virtual signals are compared to the real-time data, and if the difference between the two signals exceeds a certain tolerance, corrective action to the data and (or) the gaging station can be taken. The ADAM software is automated so that, each morning, the real-time EDEN data are compared to the inferential sensor signals and digital reports highlighting potential erroneous real-time data are generated for appropriate support personnel. The development and application of inferential sensors is easily transferable to other real-time hydrologic monitoring networks.

Implementation Of Secure 6LoWPAN Communications For Tactical Wireless Sensor Networks

DTIC Science & Technology

2016-09-01

wireless sensor networks (WSN) consist of power -constrained devices spread throughout a region-of-interest to provide data extraction in real time...1  A.  LOW POWER WIRELESS SENSOR NETWORKS ............................1  B.  INTRODUCTION TO...communication protocol for low power wireless personal area networks Since the IEEE 802.15.4 standard only defines the first two layers of the Open

Providing Web Interfaces to the NSF EarthScope USArray Transportable Array

NASA Astrophysics Data System (ADS)

Vernon, Frank; Newman, Robert; Lindquist, Kent

2010-05-01

Since April 2004 the EarthScope USArray seismic network has grown to over 850 broadband stations that stream multi-channel data in near real-time to the Array Network Facility in San Diego. Providing secure, yet open, access to real-time and archived data for a broad range of audiences is best served by a series of platform agnostic low-latency web-based applications. We present a framework of tools that mediate between the world wide web and Boulder Real Time Technologies Antelope Environmental Monitoring System data acquisition and archival software. These tools provide comprehensive information to audiences ranging from network operators and geoscience researchers, to funding agencies and the general public. This ranges from network-wide to station-specific metadata, state-of-health metrics, event detection rates, archival data and dynamic report generation over a station's two year life span. Leveraging open source web-site development frameworks for both the server side (Perl, Python and PHP) and client-side (Flickr, Google Maps/Earth and jQuery) facilitates the development of a robust extensible architecture that can be tailored on a per-user basis, with rapid prototyping and development that adheres to web-standards. Typical seismic data warehouses allow online users to query and download data collected from regional networks, without the scientist directly visually assessing data coverage and/or quality. Using a suite of web-based protocols, we have recently developed an online seismic waveform interface that directly queries and displays data from a relational database through a web-browser. Using the Python interface to Datascope and the Python-based Twisted network package on the server side, and the jQuery Javascript framework on the client side to send and receive asynchronous waveform queries, we display broadband seismic data using the HTML Canvas element that is globally accessible by anyone using a modern web-browser. We are currently creating additional interface tools to create a rich-client interface for accessing and displaying seismic data that can be deployed to any system running the Antelope Real Time System. The software is freely available from the Antelope contributed code Git repository (http://www.antelopeusersgroup.org).

Real-Time GPS Monitoring for Earthquake Rapid Assessment in the San Francisco Bay Area

NASA Astrophysics Data System (ADS)

Guillemot, C.; Langbein, J. O.; Murray, J. R.

2012-12-01

The U.S. Geological Survey Earthquake Science Center has deployed a network of eight real-time Global Positioning System (GPS) stations in the San Francisco Bay area and is implementing software applications to continuously evaluate the status of the deformation within the network. Real-time monitoring of the station positions is expected to provide valuable information for rapidly estimating source parameters should a large earthquake occur in the San Francisco Bay area. Because earthquake response applications require robust data access, as a first step we have developed a suite of web-based applications which are now routinely used to monitor the network's operational status and data streaming performance. The web tools provide continuously updated displays of important telemetry parameters such as data latency and receive rates, as well as source voltage and temperature information within each instrument enclosure. Automated software on the backend uses the streaming performance data to mitigate the impact of outages, radio interference and bandwidth congestion on deformation monitoring operations. A separate set of software applications manages the recovery of lost data due to faulty communication links. Displacement estimates are computed in real-time for various combinations of USGS, Plate Boundary Observatory (PBO) and Bay Area Regional Deformation (BARD) network stations. We are currently comparing results from two software packages (one commercial and one open-source) used to process 1-Hz data on the fly and produce estimates of differential positions. The continuous monitoring of telemetry makes it possible to tune the network to minimize the impact of transient interruptions of the data flow, from one or more stations, on the estimated positions. Ongoing work is focused on using data streaming performance history to optimize the quality of the position, reduce drift and outliers by switching to the best set of stations within the network, and automatically select the "next best" station to use as reference. We are also working towards minimizing the loss of streamed data during concurrent data downloads by improving file management on the GPS receivers.

Real-time, interactive animation of deformable two- and three-dimensional objects

DOEpatents

Desbrun, Mathieu; Schroeder, Peter; Meyer, Mark; Barr, Alan H.

2003-06-03

A method of updating in real-time the locations and velocities of mass points of a two- or three-dimensional object represented by a mass-spring system. A modified implicit Euler integration scheme is employed to determine the updated locations and velocities. In an optional post-integration step, the updated locations are corrected to preserve angular momentum. A processor readable medium and a network server each tangibly embodying the method are also provided. A system comprising a processor in combination with the medium, and a system comprising the server in combination with a client for accessing the server over a computer network, are also provided.

Cyber-Critical Infrastructure Protection Using Real-Time Payload-Based Anomaly Detection

NASA Astrophysics Data System (ADS)

Düssel, Patrick; Gehl, Christian; Laskov, Pavel; Bußer, Jens-Uwe; Störmann, Christof; Kästner, Jan

With an increasing demand of inter-connectivity and protocol standardization modern cyber-critical infrastructures are exposed to a multitude of serious threats that may give rise to severe damage for life and assets without the implementation of proper safeguards. Thus, we propose a method that is capable to reliably detect unknown, exploit-based attacks on cyber-critical infrastructures carried out over the network. We illustrate the effectiveness of the proposed method by conducting experiments on network traffic that can be found in modern industrial control systems. Moreover, we provide results of a throughput measuring which demonstrate the real-time capabilities of our system.

Social Network Analysis of Crowds

DTIC Science & Technology

2009-08-06

crowd responses to non-lethal weapons d tan sys ems – Prior, existing social relationships – Real time social interactions – Formal/informal...Crowd Behavior Testbed Layout Video Cameras on Trusses Importance of Social Factors • Response to non-lethal weapons fire depends on social ... relationships among crowd members – Pre-existing Personal Relationships – Ongoing Real Time Social Interactions – Formal/Informal Hierarchies • Therefore

Transmission of live laparoscopic surgery over the Internet2.

PubMed

Damore, L J; Johnson, J A; Dixon, R S; Iverson, M A; Ellison, E C; Melvin, W S

1999-11-01

Video broadcasting of surgical procedures is an important tool for education, training, and consultation. Current video conferencing systems are expensive and time-consuming and require preplanning. Real-time Internet video is known for its poor quality and relies on the equipment and the speed of the connection. The Internet2, a new high-speed (up to 2,048 Mbps), large bandwidth data network presently connects more than 100 universities and corporations. We have successfully used the Internet2 to broadcast the first real-time, high-quality audio/video program from a live laparoscopic operation to distant points. Video output of the laparoscopic camera and audio from a wireless microphone were broadcast to distant sites using a proprietary, PC-based implementation of H.320 video conferencing over a TCP/IP network connected to the Internet2. The receiving sites participated in two-way, real-time video and audio communications and graded the quality of the signal they received. On August 25, 1998, a laparoscopic Nissen fundoplication was transmitted to Internet2 stations in Colorado, Pennsylvania, and to an Internet station in New York. On September 28 and 29, 1998, we broadcast laparoscopic operations throughout both days to the Internet2 Fall Conference in San Francisco, California. Most recently, on February 24, 1999, we transmitted a laparoscopic Heller myotomy to the Abilene Network Launch Event in Washington, DC. The Internet2 is currently able to provide the bandwidth needed for a turn-key video conferencing system with high-resolution, real-time transmission. The system could be used for a variety of teaching and educational programs for experienced surgeons, residents, and medical students.

BIO-Plex Information System Concept

NASA Technical Reports Server (NTRS)

Jones, Harry; Boulanger, Richard; Arnold, James O. (Technical Monitor)

1999-01-01

This paper describes a suggested design for an integrated information system for the proposed BIO-Plex (Bioregenerative Planetary Life Support Systems Test Complex) at Johnson Space Center (JSC), including distributed control systems, central control, networks, database servers, personal computers and workstations, applications software, and external communications. The system will have an open commercial computing and networking, architecture. The network will provide automatic real-time transfer of information to database server computers which perform data collection and validation. This information system will support integrated, data sharing applications for everything, from system alarms to management summaries. Most existing complex process control systems have information gaps between the different real time subsystems, between these subsystems and central controller, between the central controller and system level planning and analysis application software, and between the system level applications and management overview reporting. An integrated information system is vitally necessary as the basis for the integration of planning, scheduling, modeling, monitoring, and control, which will allow improved monitoring and control based on timely, accurate and complete data. Data describing the system configuration and the real time processes can be collected, checked and reconciled, analyzed and stored in database servers that can be accessed by all applications. The required technology is available. The only opportunity to design a distributed, nonredundant, integrated system is before it is built. Retrofit is extremely difficult and costly.

Design and Implementation of an Architectural Framework for Web Portals in a Ubiquitous Pervasive Environment

PubMed Central

Raza, Muhammad Taqi; Yoo, Seung-Wha; Kim, Ki-Hyung; Joo, Seong-Soon; Jeong, Wun-Cheol

2009-01-01

Web Portals function as a single point of access to information on the World Wide Web (WWW). The web portal always contacts the portal’s gateway for the information flow that causes network traffic over the Internet. Moreover, it provides real time/dynamic access to the stored information, but not access to the real time information. This inherent functionality of web portals limits their role for resource constrained digital devices in the Ubiquitous era (U-era). This paper presents a framework for the web portal in the U-era. We have introduced the concept of Local Regions in the proposed framework, so that the local queries could be solved locally rather than having to route them over the Internet. Moreover, our framework enables one-to-one device communication for real time information flow. To provide an in-depth analysis, firstly, we provide an analytical model for query processing at the servers for our framework-oriented web portal. At the end, we have deployed a testbed, as one of the world’s largest IP based wireless sensor networks testbed, and real time measurements are observed that prove the efficacy and workability of the proposed framework. PMID:22346693

Design and implementation of an architectural framework for web portals in a ubiquitous pervasive environment.

PubMed

Raza, Muhammad Taqi; Yoo, Seung-Wha; Kim, Ki-Hyung; Joo, Seong-Soon; Jeong, Wun-Cheol

2009-01-01

Web Portals function as a single point of access to information on the World Wide Web (WWW). The web portal always contacts the portal's gateway for the information flow that causes network traffic over the Internet. Moreover, it provides real time/dynamic access to the stored information, but not access to the real time information. This inherent functionality of web portals limits their role for resource constrained digital devices in the Ubiquitous era (U-era). This paper presents a framework for the web portal in the U-era. We have introduced the concept of Local Regions in the proposed framework, so that the local queries could be solved locally rather than having to route them over the Internet. Moreover, our framework enables one-to-one device communication for real time information flow. To provide an in-depth analysis, firstly, we provide an analytical model for query processing at the servers for our framework-oriented web portal. At the end, we have deployed a testbed, as one of the world's largest IP based wireless sensor networks testbed, and real time measurements are observed that prove the efficacy and workability of the proposed framework.

Communicability across evolving networks.

PubMed

Grindrod, Peter; Parsons, Mark C; Higham, Desmond J; Estrada, Ernesto

2011-04-01

Many natural and technological applications generate time-ordered sequences of networks, defined over a fixed set of nodes; for example, time-stamped information about "who phoned who" or "who came into contact with who" arise naturally in studies of communication and the spread of disease. Concepts and algorithms for static networks do not immediately carry through to this dynamic setting. For example, suppose A and B interact in the morning, and then B and C interact in the afternoon. Information, or disease, may then pass from A to C, but not vice versa. This subtlety is lost if we simply summarize using the daily aggregate network given by the chain A-B-C. However, using a natural definition of a walk on an evolving network, we show that classic centrality measures from the static setting can be extended in a computationally convenient manner. In particular, communicability indices can be computed to summarize the ability of each node to broadcast and receive information. The computations involve basic operations in linear algebra, and the asymmetry caused by time's arrow is captured naturally through the noncommutativity of matrix-matrix multiplication. Illustrative examples are given for both synthetic and real-world communication data sets. We also discuss the use of the new centrality measures for real-time monitoring and prediction.

The California Integrated Seismic Network

NASA Astrophysics Data System (ADS)

Hellweg, M.; Given, D.; Hauksson, E.; Neuhauser, D.; Oppenheimer, D.; Shakal, A.

2007-05-01

The mission of the California Integrated Seismic Network (CISN) is to operate a reliable, modern system to monitor earthquakes throughout the state; to generate and distribute information in real-time for emergency response, for the benefit of public safety, and for loss mitigation; and to collect and archive data for seismological and earthquake engineering research. To meet these needs, the CISN operates data processing and archiving centers, as well as more than 3000 seismic stations. Furthermore, the CISN is actively developing and enhancing its infrastructure, including its automated processing and archival systems. The CISN integrates seismic and strong motion networks operated by the University of California Berkeley (UCB), the California Institute of Technology (Caltech), and the United States Geological Survey (USGS) offices in Menlo Park and Pasadena, as well as the USGS National Strong Motion Program (NSMP), and the California Geological Survey (CGS). The CISN operates two earthquake management centers (the NCEMC and SCEMC) where statewide, real-time earthquake monitoring takes place, and an engineering data center (EDC) for processing strong motion data and making it available in near real-time to the engineering community. These centers employ redundant hardware to minimize disruptions to the earthquake detection and processing systems. At the same time, dual feeds of data from a subset of broadband and strong motion stations are telemetered in real- time directly to both the NCEMC and the SCEMC to ensure the availability of statewide data in the event of a catastrophic failure at one of these two centers. The CISN uses a backbone T1 ring (with automatic backup over the internet) to interconnect the centers and the California Office of Emergency Services. The T1 ring enables real-time exchange of selected waveforms, derived ground motion data, phase arrivals, earthquake parameters, and ShakeMaps. With the goal of operating similar and redundant statewide earthquake processing systems at both real-time EMCs, the CISN is currently adopting and enhancing the database-centric, earthquake processing and analysis software originally developed for the Caltech/USGS Pasadena TriNet project. Earthquake data and waveforms are made available to researchers and to the public in near real-time through the CISN's Northern and Southern California Eathquake Data Centers (NCEDC and SCEDC) and through the USGS Earthquake Notification System (ENS). The CISN partners have developed procedures to automatically exchange strong motion data, both waveforms and peak parameters, for use in ShakeMap and in the rapid engineering reports which are available near real-time through the strong motion EDC.

Real-time Medical Emergency Response System: Exploiting IoT and Big Data for Public Health.

PubMed

Rathore, M Mazhar; Ahmad, Awais; Paul, Anand; Wan, Jiafu; Zhang, Daqiang

2016-12-01

Healthy people are important for any nation's development. Use of the Internet of Things (IoT)-based body area networks (BANs) is increasing for continuous monitoring and medical healthcare in order to perform real-time actions in case of emergencies. However, in the case of monitoring the health of all citizens or people in a country, the millions of sensors attached to human bodies generate massive volume of heterogeneous data, called "Big Data." Processing Big Data and performing real-time actions in critical situations is a challenging task. Therefore, in order to address such issues, we propose a Real-time Medical Emergency Response System that involves IoT-based medical sensors deployed on the human body. Moreover, the proposed system consists of the data analysis building, called "Intelligent Building," depicted by the proposed layered architecture and implementation model, and it is responsible for analysis and decision-making. The data collected from millions of body-attached sensors is forwarded to Intelligent Building for processing and for performing necessary actions using various units such as collection, Hadoop Processing (HPU), and analysis and decision. The feasibility and efficiency of the proposed system are evaluated by implementing the system on Hadoop using an UBUNTU 14.04 LTS coreTMi5 machine. Various medical sensory datasets and real-time network traffic are considered for evaluating the efficiency of the system. The results show that the proposed system has the capability of efficiently processing WBAN sensory data from millions of users in order to perform real-time responses in case of emergencies.

Functional cliques in the amygdala and related brain networks driven by fear assessment acquired during movie viewing.

PubMed

Kinreich, Sivan; Intrator, Nathan; Hendler, Talma

2011-01-01

One of the greatest challenges involved in studying the brain mechanisms of fear is capturing the individual's unique instantaneous experience. Brain imaging studies to date commonly sacrifice valuable information regarding the individual real-time conscious experience, especially when focusing on elucidating the amygdala's activity. Here, we assumed that by using a minimally intrusive cue along with applying a robust clustering approach to probe the amygdala, it would be possible to rate fear in real time and to derive the related network of activation. During functional magnetic resonance imaging scanning, healthy volunteers viewed two excerpts from horror movies and were periodically auditory cued to rate their instantaneous experience of "I'm scared." Using graph theory and community mathematical concepts, data-driven clustering of the fear-related functional cliques in the amygdala was performed guided by the individually marked periods of heightened fear. Individually tailored functions derived from these amygdala activation cliques were subsequently applied as general linear model predictors to a whole-brain analysis to reveal the correlated networks. Our results suggest that by using a localized robust clustering approach, it is possible to probe activation in the right dorsal amygdala that is directly related to individual real-time emotional experience. Moreover, this fear-evoked amygdala revealed two opposing networks of co-activation and co-deactivation, which correspond to vigilance and rest-related circuits, respectively.

Properties of four real world collaboration--competition networks

NASA Astrophysics Data System (ADS)

Fu, Chun-Hua; Xu, Xiu-Lian; He, Da-Ren

2009-03-01

Our research group has empirically investigated 9 real world collaboration networks and 25 real world cooperation-competition networks. Among the 34 real world systems, all the 9 real world collaboration networks and 6 real world cooperation-competition networks show the unimodal act-size distribution and the shifted power law distribution of degree and act-degree. We have proposed a collaboration network evolution model for an explanation of the rules [1]. The other 14 real world cooperation-competition networks show that the act-size distributions are not unimodal; instead, they take qualitatively the same shifted power law forms as the degree and act-degree distributions. The properties of four systems (the main land movie film network, Beijing restaurant network, 2004 Olympic network, and Tao-Bao notebook computer sale network) are reported in detail as examples. Via a numerical simulation, we show that the new rule can still be explained by the above-mentioned model. [1] H. Chang, B. B. Su, et al. Phsica A, 2007, 383: 687-702.

Susceptible-infected-susceptible epidemics on networks with general infection and cure times.

PubMed

Cator, E; van de Bovenkamp, R; Van Mieghem, P

2013-06-01

The classical, continuous-time susceptible-infected-susceptible (SIS) Markov epidemic model on an arbitrary network is extended to incorporate infection and curing or recovery times each characterized by a general distribution (rather than an exponential distribution as in Markov processes). This extension, called the generalized SIS (GSIS) model, is believed to have a much larger applicability to real-world epidemics (such as information spread in online social networks, real diseases, malware spread in computer networks, etc.) that likely do not feature exponential times. While the exact governing equations for the GSIS model are difficult to deduce due to their non-Markovian nature, accurate mean-field equations are derived that resemble our previous N-intertwined mean-field approximation (NIMFA) and so allow us to transfer the whole analytic machinery of the NIMFA to the GSIS model. In particular, we establish the criterion to compute the epidemic threshold in the GSIS model. Moreover, we show that the average number of infection attempts during a recovery time is the more natural key parameter, instead of the effective infection rate in the classical, continuous-time SIS Markov model. The relative simplicity of our mean-field results enables us to treat more general types of SIS epidemics, while offering an easier key parameter to measure the average activity of those general viral agents.

Susceptible-infected-susceptible epidemics on networks with general infection and cure times

NASA Astrophysics Data System (ADS)

Cator, E.; van de Bovenkamp, R.; Van Mieghem, P.

2013-06-01

The classical, continuous-time susceptible-infected-susceptible (SIS) Markov epidemic model on an arbitrary network is extended to incorporate infection and curing or recovery times each characterized by a general distribution (rather than an exponential distribution as in Markov processes). This extension, called the generalized SIS (GSIS) model, is believed to have a much larger applicability to real-world epidemics (such as information spread in online social networks, real diseases, malware spread in computer networks, etc.) that likely do not feature exponential times. While the exact governing equations for the GSIS model are difficult to deduce due to their non-Markovian nature, accurate mean-field equations are derived that resemble our previous N-intertwined mean-field approximation (NIMFA) and so allow us to transfer the whole analytic machinery of the NIMFA to the GSIS model. In particular, we establish the criterion to compute the epidemic threshold in the GSIS model. Moreover, we show that the average number of infection attempts during a recovery time is the more natural key parameter, instead of the effective infection rate in the classical, continuous-time SIS Markov model. The relative simplicity of our mean-field results enables us to treat more general types of SIS epidemics, while offering an easier key parameter to measure the average activity of those general viral agents.

Urban Mobility and Location-Based Social Networks: Social, Economic and Environmental Incentives

ERIC Educational Resources Information Center

Zhang, Ke

2016-01-01

Location-based social networks (LBSNs) have recently attracted the interest of millions of users who can now not only connect and interact with their friends--as it also happens in traditional online social networks--but can also voluntarily share their whereabouts in real time. A location database is the backbone of a location-based social…

Optimizing network connectivity for mobile health technologies in sub-Saharan Africa.

PubMed

Siedner, Mark J; Lankowski, Alexander; Musinga, Derrick; Jackson, Jonathon; Muzoora, Conrad; Hunt, Peter W; Martin, Jeffrey N; Bangsberg, David R; Haberer, Jessica E

2012-01-01

Mobile health (mHealth) technologies hold incredible promise to improve healthcare delivery in resource-limited settings. Network reliability across large catchment areas can be a major challenge. We performed an analysis of network failure frequency as part of a study of real-time adherence monitoring in rural Uganda. We hypothesized that the addition of short messaging service (SMS+GPRS) to the standard cellular network modality (GPRS) would reduce network disruptions and improve transmission of data. Participants were enrolled in a study of real-time adherence monitoring in southwest Uganda. In June 2011, we began using Wisepill devices that transmit data each time the pill bottle is opened. We defined network failures as medication interruptions of >48 hours duration that were transmitted when network connectivity was re-established. During the course of the study, we upgraded devices from GPRS to GPRS+SMS compatibility. We compared network failure rates between GPRS and GPRS+SMS periods and created geospatial maps to graphically demonstrate patterns of connectivity. One hundred fifty-seven participants met inclusion criteria of seven days of SMS and seven days of SMS+GPRS observation time. Seventy-three percent were female, median age was 40 years (IQR 33-46), 39% reported >1-hour travel time to clinic and 17% had home electricity. One hundred one had GPS coordinates recorded and were included in the geospatial maps. The median number of network failures per person-month for the GPRS and GPRS+SMS modalities were 1.5 (IQR 1.0-2.2) and 0.3 (IQR 0-0.9) respectively, (mean difference 1.2, 95%CI 1.0-1.3, p-value<0.0001). Improvements in network connectivity were notable throughout the region. Study costs increased by approximately $1USD per person-month. Addition of SMS to standard GPRS cellular network connectivity can significantly reduce network connection failures for mobile health applications in remote areas. Projects depending on mobile health data in resource-limited settings should consider this upgrade to optimize mHealth applications.

Interaction of multiple networks modulated by the working memory training based on real-time fMRI

NASA Astrophysics Data System (ADS)

Shen, Jiahui; Zhang, Gaoyan; Zhu, Chaozhe; Yao, Li; Zhao, Xiaojie

2015-03-01

Neuroimaging studies of working memory training have identified the alteration of brain activity as well as the regional interactions within the functional networks such as central executive network (CEN) and default mode network (DMN). However, how the interaction within and between these multiple networks is modulated by the training remains unclear. In this paper, we examined the interaction of three training-induced brain networks during working memory training based on real-time functional magnetic resonance imaging (rtfMRI). Thirty subjects assigned to the experimental and control group respectively participated in two times training separated by seven days. Three networks including silence network (SN), CEN and DMN were identified by the training data with the calculated function connections within each network. Structural equation modeling (SEM) approach was used to construct the directional connectivity patterns. The results showed that the causal influences from the percent signal changes of target ROI to the SN were positively changed in both two groups, as well as the causal influence from the SN to CEN was positively changed in experimental group but negatively changed in control group from the SN to DMN. Further correlation analysis of the changes in each network with the behavioral improvements showed that the changes in SN were stronger positively correlated with the behavioral improvement of letter memory task. These findings indicated that the SN was not only a switch between the target ROI and the other networks in the feedback training but also an essential factor to the behavioral improvement.

A pilot study on diagnostic sensor networks for structure health monitoring.

DOT National Transportation Integrated Search

2013-08-01

The proposal was submitted in an effort to obtain some preliminary results on using sensor networks for real-time structure health : monitoring. The proposed work has twofold: to develop and validate an elective algorithm for the diagnosis of coupled...

HRLSim: a high performance spiking neural network simulator for GPGPU clusters.

PubMed

Minkovich, Kirill; Thibeault, Corey M; O'Brien, Michael John; Nogin, Aleksey; Cho, Youngkwan; Srinivasa, Narayan

2014-02-01

Modeling of large-scale spiking neural models is an important tool in the quest to understand brain function and subsequently create real-world applications. This paper describes a spiking neural network simulator environment called HRL Spiking Simulator (HRLSim). This simulator is suitable for implementation on a cluster of general purpose graphical processing units (GPGPUs). Novel aspects of HRLSim are described and an analysis of its performance is provided for various configurations of the cluster. With the advent of inexpensive GPGPU cards and compute power, HRLSim offers an affordable and scalable tool for design, real-time simulation, and analysis of large-scale spiking neural networks.

Network Reduction Algorithm for Developing Distribution Feeders for Real-Time Simulators: Preprint

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

Nagarajan, Adarsh; Nelson, Austin; Prabakar, Kumaraguru

As advanced grid-support functions (AGF) become more widely used in grid-connected photovoltaic (PV) inverters, utilities are increasingly interested in their impacts when implemented in the field. These effects can be understood by modeling feeders in real-time systems and testing PV inverters using power hardware-in-the-loop (PHIL) techniques. This paper presents a novel feeder model reduction algorithm using a Monte Carlo method that enables large feeders to be solved and operated on real-time computing platforms. Two Hawaiian Electric feeder models in Synergi Electric's load flow software were converted to reduced order models in OpenDSS, and subsequently implemented in the OPAL-RT real-time digitalmore » testing platform. Smart PV inverters were added to the real-time model with AGF responses modeled after characterizing commercially available hardware inverters. Finally, hardware inverters were tested in conjunction with the real-time model using PHIL techniques so that the effects of AGFs on the choice feeders could be analyzed.« less

Real-time determination of fringe pattern frequencies: An application to pressure measurement

NASA Astrophysics Data System (ADS)

Sciammarella, Cesar A.; Piroozan, Parham

2007-05-01

Retrieving information in real time from fringe patterns is a topic of a great deal of interest in scientific and engineering applications of optical methods. This paper presents a method for fringe frequency determination based on the capability of neural networks to recognize signals that are similar but not identical to signals used to train the neural network. Sampled patterns are generated by calibration and stored in memory. Incoming patterns are analyzed by a back-propagation neural network at the speed of the recording device, a CCD camera. This method of information retrieval is utilized to measure pressures on a boundary layer flow. The sensor combines optics and electronics to analyze dynamic pressure distributions and to feed information to a control system that is capable to preserve the stability of the flow.

Dynamic Control of Adsorption Sensitivity for Photo-EMF-Based Ammonia Gas Sensors Using a Wireless Network

PubMed Central

Vashpanov, Yuriy; Choo, Hyunseung; Kim, Dongsoo Stephen

2011-01-01

This paper proposes an adsorption sensitivity control method that uses a wireless network and illumination light intensity in a photo-electromagnetic field (EMF)-based gas sensor for measurements in real time of a wide range of ammonia concentrations. The minimum measurement error for a range of ammonia concentration from 3 to 800 ppm occurs when the gas concentration magnitude corresponds with the optimal intensity of the illumination light. A simulation with LabView-engineered modules for automatic control of a new intelligent computer system was conducted to improve measurement precision over a wide range of gas concentrations. This gas sensor computer system with wireless network technology could be useful in the chemical industry for automatic detection and measurement of hazardous ammonia gas levels in real time. PMID:22346680

A quantum network of clocks

NASA Astrophysics Data System (ADS)

Komar, Peter; Kessler, Eric; Bishof, Michael; Jiang, Liang; Sorensen, Anders; Ye, Jun; Lukin, Mikhail

2014-05-01

Shared timing information constitutes a key resource for positioning and navigation with a direct correspondence between timing accuracy and precision in applications such as the Global Positioning System (GPS). By combining precision metrology and quantum networks, we propose here a quantum, cooperative protocol for the operation of a network consisting of geographically remote optical atomic clocks. Using non-local entangled states, we demonstrate an optimal utilization of the global network resources, and show that such a network can be operated near the fundamental limit set by quantum theory yielding an ultra-precise clock signal. Furthermore, the internal structure of the network, combined with basic techniques from quantum communication, guarantees security both from internal and external threats. Realization of such a global quantum network of clocks may allow construction of a real-time single international time scale (world clock) with unprecedented stability and accuracy. See also: Komar et al. arXiv:1310.6045 (2013) and Kessler et al. arXiv:1310.6043 (2013).

Attack tolerance of correlated time-varying social networks with well-defined communities

NASA Astrophysics Data System (ADS)

Sur, Souvik; Ganguly, Niloy; Mukherjee, Animesh

2015-02-01

In this paper, we investigate the efficiency and the robustness of information transmission for real-world social networks, modeled as time-varying instances, under targeted attack in shorter time spans. We observe that these quantities are markedly higher than that of the randomized versions of the considered networks. An important factor that drives this efficiency or robustness is the presence of short-time correlations across the network instances which we quantify by a novel metric the-edge emergence factor, denoted as ξ. We find that standard targeted attacks are not effective in collapsing this network structure. Remarkably, if the hourly community structures of the temporal network instances are attacked with the largest size community attacked first, the second largest next and so on, the network soon collapses. This behavior, we show is an outcome of the fact that the edge emergence factor bears a strong positive correlation with the size ordered community structures.

A quantum network of clocks

NASA Astrophysics Data System (ADS)

Kómár, P.; Kessler, E. M.; Bishof, M.; Jiang, L.; Sørensen, A. S.; Ye, J.; Lukin, M. D.

2014-08-01

The development of precise atomic clocks plays an increasingly important role in modern society. Shared timing information constitutes a key resource for navigation with a direct correspondence between timing accuracy and precision in applications such as the Global Positioning System. By combining precision metrology and quantum networks, we propose a quantum, cooperative protocol for operating a network of geographically remote optical atomic clocks. Using nonlocal entangled states, we demonstrate an optimal utilization of global resources, and show that such a network can be operated near the fundamental precision limit set by quantum theory. Furthermore, the internal structure of the network, combined with quantum communication techniques, guarantees security both from internal and external threats. Realization of such a global quantum network of clocks may allow construction of a real-time single international time scale (world clock) with unprecedented stability and accuracy.

Birmingham Urban Climate Laboratory (BUCL): Experiences, Challenges and Applications of an Urban Temperature Network

NASA Astrophysics Data System (ADS)

Muller, Catherine; Chapman, Lee; Young, Duick; Grimmond, Sue; Cai, Xiaoming

2013-04-01

The Birmingham Urban Climate Laboratory (BUCL) has recently been established by the University of Birmingham. BUCL is an in-situ, real-time urban network that will incorporate 3 nested networks - a wide-array of 25 weather stations, a dense array of 131 low-cost air temperature sensors and a fine-array of temperature sensor across the city-centre (50/km^2) - with the primary aim of monitoring air temperatures across a morphologically-heterogeneous urban conurbation for a variety of applications. During its installation there have been a number of challenges to overcome, including siting equipment in suitable urban locations, ensuring that the measurements were 'representative' of the local-scale climate, managing a large, near real-time data set and implementing QA/QC procedures. From these experiences, the establishment of a standardised urban meteorological network metadata protocol has been proposed in order to improve data quality, to ensure the end-user has access to all the supplementary information they would require for conducting valid analyses and to encourage the adequate recording and documentation of any changes to in-situ urban networks over time. This paper will provide an introduction to the BUCL in-situ network, give an overview of the challenges and experiences gained from its implementation, and finally discuss the proposed applications of the network, including its use in remote sensing observations of urban temperatures, as well as health and infrastructure applications.

Oscillations in interconnected complex networks under intentional attack

NASA Astrophysics Data System (ADS)

Zhang, Wen-Ping; Xia, Yongxiang; Tan, Fei

2016-01-01

Many real-world networks are interconnected with each other. In this paper, we study the traffic dynamics in interconnected complex networks under an intentional attack. We find that with the shortest time delay routing strategy, the traffic dynamics can show the stable state, periodic, quasi-periodic and chaotic oscillations, when the capacity redundancy parameter changes. Moreover, compared with isolated complex networks, oscillations always take place in interconnected networks more easily. Thirdly, in interconnected networks, oscillations are affected strongly by the coupling probability and coupling preference.

Aeronautical-Satellite-Assisted Process Being Developed for Information Exchange Through Network Technologies (Aero-SAPIENT)

NASA Technical Reports Server (NTRS)

Zernic, Michael J.

2001-01-01

Communications technologies are being developed to address safety issues during aviation travel. Some of these technologies enable the aircraft to be in constant bidirectional communications with necessary systems, people, and other aircraft that are not currently in place today. Networking technologies, wireless datalinks, and advanced avionics techniques are areas of particular importance that the NASA Glenn Research Center has contributed. Glenn, in conjunction with the NASA Ames Research Center, NASA Dryden Flight Research Center, and NASA Langley Research Center, is investigating methods and applications that would utilize these communications technologies. In mid-June 2000, the flight readiness of the network and communications technologies were demonstrated via a simulated aircraft. A van simulating an aircraft was equipped with advanced phased-array antennas (Advanced Communications/Air Traffic Management (AC/ATM) Advanced Air Transportation Technologies (AATT) project) that used commercial Ku-band satellite communications to connect Glenn, Dryden, and Ames in a combined system ground test. This test simulated air-ground bidirectional transport of real-time digital audio, text, and video data via a hybrid network configuration that demonstrated the flight readiness of the network and communications technologies. Specifically, a Controller Pilot Data Link Communications application was used with other applications to demonstrate a multiprotocol capability via Internet-protocol encapsulated ATN (Aeronautical Telecommunications Network) data packets. The significance of this combined ground test is its contribution to the Aero Information Technology Base Program Level I milestone (Software Technology investment area) of a real-time data link for the National Airspace System. The objective of this milestone was to address multiprotocol technology applicable for real-time data links between aircraft, a satellite, and the ground as well as the ability to distribute flight data with multilevel priorities among several sites.

Remote consultation and diagnosis in medical imaging using a global PACS backbone network

NASA Astrophysics Data System (ADS)

Martinez, Ralph; Sutaria, Bijal N.; Kim, Jinman; Nam, Jiseung

1993-10-01

A Global PACS is a national network which interconnects several PACS networks at medical and hospital complexes using a national backbone network. A Global PACS environment enables new and beneficial operations between radiologists and physicians, when they are located in different geographical locations. One operation allows the radiologist to view the same image folder at both Local and Remote sites so that a diagnosis can be performed. The paper describes the user interface, database management, and network communication software which has been developed in the Computer Engineering Research Laboratory and Radiology Research Laboratory. Specifically, a design for a file management system in a distributed environment is presented. In the remote consultation and diagnosis operation, a set of images is requested from the database archive system and sent to the Local and Remote workstation sites on the Global PACS network. Viewing the same images, the radiologists use pointing overlay commands, or frames to point out features on the images. Each workstation transfers these frames, to the other workstation, so that an interactive session for diagnosis takes place. In this phase, we use fixed frames and variable size frames, used to outline an object. The data pockets for these frames traverses the national backbone in real-time. We accomplish this feature by using TCP/IP protocol sockets for communications. The remote consultation and diagnosis operation has been tested in real-time between the University Medical Center and the Bowman Gray School of Medicine at Wake Forest University, over the Internet. In this paper, we show the feasibility of the operation in a Global PACS environment. Future improvements to the system will include real-time voice and interactive compressed video scenarios.

Evaluation of the Horizontal and Vertical Accuracy of GNSS Survey Observations from a Real-Time Network

NASA Astrophysics Data System (ADS)

Allahyari, M.; Olsen, M. J.; Gillins, D. T.; Dennis, M. L.

2016-12-01

Many current surveying standards in the United States require several long-duration, static Global Navigation Satellite System (GNSS) observations to derive high-accuracy geodetic coordinates. However, over the past decade, many entities have established real-time GNSS networks (RTNs), which could reduce the field time for establishing geodetic control from hours to minutes. To evaluate the accuracy of RTN GNSS observations, data collected from two National Geodetic Survey (NGS) surveys in South Carolina and Oregon were studied. The objectives were to: 1) determine the accuracy of a real-time observation as a function of duration; 2) examine the influence of including GLONASS (Russia's version of GPS); 3) compare results using a single base to the full RTN network solution; and 4) assess the effect of baseline length on accuracy. In South Carolina, 360 observations ranging from 5 to 600 seconds were collected on 20 passive marks using RTN and single-base solutions, both with GPS+GLONASS and GPS-only. In Oregon, 18 passive marks were observed from 5 to 900 seconds using GPS-only with the RTN, and with GPS+GLONASS and GPS-only from a single-base. To develop "truth" coordinates, at least 30 hours of static GPS data were also collected on all marks. Each static survey session was post-processed in OPUS-Projects, and the resulting vectors were used to build survey networks that were least-squares adjusted using the NGS software ADJUST. The resulting coordinates provided the basis for evaluating the accuracy of the real-time observations. Results from this study indicate great potential in the use of RTNs for accurate derivation of geodetic coordinates. Both case studies showed an optimal observation duration of 180 seconds. RTN data tended to be more accurate and consistent than single-base data, and GLONASS slightly improved accuracy. A key benefit of GLONASS was the ability to obtain more fixed solutions at longer baseline lengths than single-base solutions.

Real-time control systems: feedback, scheduling and robustness

NASA Astrophysics Data System (ADS)

Simon, Daniel; Seuret, Alexandre; Sename, Olivier

2017-08-01

The efficient control of real-time distributed systems, where continuous components are governed through digital devices and communication networks, needs a careful examination of the constraints arising from the different involved domains inside co-design approaches. Thanks to the robustness of feedback control, both new control methodologies and slackened real-time scheduling schemes are proposed beyond the frontiers between these traditionally separated fields. A methodology to design robust aperiodic controllers is provided, where the sampling interval is considered as a control variable of the system. Promising experimental results are provided to show the feasibility and robustness of the approach.

Decision support system for outage management and automated crew dispatch

DOEpatents

Kang, Ning; Mousavi, Mirrasoul

2018-01-23

A decision support system is provided for utility operations to assist with crew dispatch and restoration activities following the occurrence of a disturbance in a multiphase power distribution network, by providing a real-time visualization of possible location(s). The system covers faults that occur on fuse-protected laterals. The system uses real-time data from intelligent electronics devices coupled with other data sources such as static feeder maps to provide a complete picture of the disturbance event, guiding the utility crew to the most probable location(s). This information is provided in real-time, reducing restoration time and avoiding more costly and laborious fault location finding practices.

Point-Process Models of Social Network Interactions: Parameter Estimation and Missing Data Recovery

DTIC Science & Technology

2014-08-01

treating them as zero will have a de minimis impact on the results, but avoiding computing them (and computing with them) saves tremendous time. Set a... test the methods on simulated time series on artificial social networks, including some toy networks and some meant to resemble IkeNet. We conclude...the section by discussing the results in detail. In each of our tests we begin with a complete data set, whether it is real (IkeNet) or simulated. Then

Design and implementation of laser target simulator in hardware-in-the-loop simulation system based on LabWindows/CVI and RTX

NASA Astrophysics Data System (ADS)

Tong, Qiujie; Wang, Qianqian; Li, Xiaoyang; Shan, Bin; Cui, Xuntai; Li, Chenyu; Peng, Zhong

2016-11-01

In order to satisfy the requirements of the real-time and generality, a laser target simulator in semi-physical simulation system based on RTX+LabWindows/CVI platform is proposed in this paper. Compared with the upper-lower computers simulation platform architecture used in the most of the real-time system now, this system has better maintainability and portability. This system runs on the Windows platform, using Windows RTX real-time extension subsystem to ensure the real-time performance of the system combining with the reflective memory network to complete some real-time tasks such as calculating the simulation model, transmitting the simulation data, and keeping real-time communication. The real-time tasks of simulation system run under the RTSS process. At the same time, we use the LabWindows/CVI to compile a graphical interface, and complete some non-real-time tasks in the process of simulation such as man-machine interaction, display and storage of the simulation data, which run under the Win32 process. Through the design of RTX shared memory and task scheduling algorithm, the data interaction between the real-time tasks process of RTSS and non-real-time tasks process of Win32 is completed. The experimental results show that this system has the strongly real-time performance, highly stability, and highly simulation accuracy. At the same time, it also has the good performance of human-computer interaction.

Adaptive Video Streaming Using Bandwidth Estimation for 3.5G Mobile Network

NASA Astrophysics Data System (ADS)

Nam, Hyeong-Min; Park, Chun-Su; Jung, Seung-Won; Ko, Sung-Jea

Currently deployed mobile networks including High Speed Downlink Packet Access (HSDPA) offer only best-effort Quality of Service (QoS). In wireless best effort networks, the bandwidth variation is a critical problem, especially, for mobile devices with small buffers. This is because the bandwidth variation leads to packet losses caused by buffer overflow as well as picture freezing due to high transmission delay or buffer underflow. In this paper, in order to provide seamless video streaming over HSDPA, we propose an efficient real-time video streaming method that consists of the available bandwidth (AB) estimation for the HSDPA network and the transmission rate control to prevent buffer overflows/underflows. In the proposed method, the client estimates the AB and the estimated AB is fed back to the server through real-time transport control protocol (RTCP) packets. Then, the server adaptively adjusts the transmission rate according to the estimated AB and the buffer state obtained from the RTCP feedback information. Experimental results show that the proposed method achieves seamless video streaming over the HSDPA network providing higher video quality and lower transmission delay.

Web-based monitoring and management system for integrated enterprise-wide imaging networks

NASA Astrophysics Data System (ADS)

Ma, Keith; Slik, David; Lam, Alvin; Ng, Won

2003-05-01

Mass proliferation of IP networks and the maturity of standards has enabled the creation of sophisticated image distribution networks that operate over Intranets, Extranets, Communities of Interest (CoI) and even the public Internet. Unified monitoring, provisioning and management of such systems at the application and protocol levels represent a challenge. This paper presents a web based monitoring and management tool that employs established telecom standards for the creation of an open system that enables proactive management, provisioning and monitoring of image management systems at the enterprise level and across multi-site geographically distributed deployments. Utilizing established standards including ITU-T M.3100, and web technologies such as XML/XSLT, JSP/JSTL, and J2SE, the system allows for seamless device and protocol adaptation between multiple disparate devices. The goal has been to develop a unified interface that provides network topology views, multi-level customizable alerts, real-time fault detection as well as real-time and historical reporting of all monitored resources, including network connectivity, system load, DICOM transactions and storage capacities.

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.

A controllable sensor management algorithm capable of learning

NASA Astrophysics Data System (ADS)

Osadciw, Lisa A.; Veeramacheneni, Kalyan K.

2005-03-01

Sensor management technology progress is challenged by the geographic space it spans, the heterogeneity of the sensors, and the real-time timeframes within which plans controlling the assets are executed. This paper presents a new sensor management paradigm and demonstrates its application in a sensor management algorithm designed for a biometric access control system. This approach consists of an artificial intelligence (AI) algorithm focused on uncertainty measures, which makes the high level decisions to reduce uncertainties and interfaces with the user, integrated cohesively with a bottom up evolutionary algorithm, which optimizes the sensor network"s operation as determined by the AI algorithm. The sensor management algorithm presented is composed of a Bayesian network, the AI algorithm component, and a swarm optimization algorithm, the evolutionary algorithm. Thus, the algorithm can change its own performance goals in real-time and will modify its own decisions based on observed measures within the sensor network. The definition of the measures as well as the Bayesian network determine the robustness of the algorithm and its utility in reacting dynamically to changes in the global system.

A Nationwide Experimental Multi-Gigabit Network

DTIC Science & Technology

2003-03-01

television and cinema , and to real- time interactive teleconferencing. There is another variable which affects this happy growth in network bandwidth and...render large scientific data sets with interactive frame rates on the desktop or in an immersive virtual reality ( VR ) environment. In our design, we