Definition and evaluation of the data-link layer of PACnet

NASA Astrophysics Data System (ADS)

Alsafadi, Yasser H.; Martinez, Ralph; Sanders, William H.

1991-07-01

PACnet is a 200-500 Mbps dual-ring fiber optic network designed to implement a picture archiving and communication system (PACS) in a hospital environment. The network consists of three channels: an image transfer channel, a command and control channel, and a real-time data channel. An initial network interface unit (NIU) design for PACnet consisted of a functional description of the protocols and NIU major components. In order to develop a demonstration prototype, additional definition of protocol algorithms of each channel is necessary. Using the International Standards Organization/Open Systems Interconnection (ISO/OSI) reference model as a guide, the definition of the data link layer is extended. This definition covers interface service specifications for the two constituent sublayers: logical link control (LLC) and medium access control (MAC). Furthermore, it describes procedures for data transfer, mechanisms of error detection and fault recovery. A performance evaluation study was then made to determine how the network performs under various application scenarios. The performance evaluation study was performed using stochastic activity networks, which can formally describe the network behavior. The results of the study demonstrate the feasibility of PACnet as an integrated image, data, and voice network for PACS.

Moving Large Data Sets Over High-Performance Long Distance Networks

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

Hodson, Stephen W; Poole, Stephen W; Ruwart, Thomas

2011-04-01

In this project we look at the performance characteristics of three tools used to move large data sets over dedicated long distance networking infrastructure. Although performance studies of wide area networks have been a frequent topic of interest, performance analyses have tended to focus on network latency characteristics and peak throughput using network traffic generators. In this study we instead perform an end-to-end long distance networking analysis that includes reading large data sets from a source file system and committing large data sets to a destination file system. An evaluation of end-to-end data movement is also an evaluation of themore » system configurations employed and the tools used to move the data. For this paper, we have built several storage platforms and connected them with a high performance long distance network configuration. We use these systems to analyze the capabilities of three data movement tools: BBcp, GridFTP, and XDD. Our studies demonstrate that existing data movement tools do not provide efficient performance levels or exercise the storage devices in their highest performance modes. We describe the device information required to achieve high levels of I/O performance and discuss how this data is applicable in use cases beyond data movement performance.« less

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

NASA Astrophysics Data System (ADS)

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

2014-09-01

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

A neural network model for credit risk evaluation.

PubMed

Khashman, Adnan

2009-08-01

Credit scoring is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. This paper presents a credit risk evaluation system that uses a neural network model based on the back propagation learning algorithm. We train and implement the neural network to decide whether to approve or reject a credit application, using seven learning schemes and real world credit applications from the Australian credit approval datasets. A comparison of the system performance under the different learning schemes is provided, furthermore, we compare the performance of two neural networks; with one and two hidden layers following the ideal learning scheme. Experimental results suggest that neural networks can be effectively used in automatic processing of credit applications.

Evaluation plan for space station network interface units

NASA Technical Reports Server (NTRS)

Weaver, Alfred C.

1990-01-01

Outlined here is a procedure for evaluating network interface units (NIUs) produced for the Space Station program. The procedures should be equally applicable to the data management system (DMS) testbed NIUs produced by Honeywell and IBM. The evaluation procedures are divided into four areas. Performance measurement tools are hardware and software that must be developed in order to evaluate NIU performance. Performance tests are a series of tests, each of which documents some specific characteristic of NIU and/or network performance. In general, these performance tests quantify the speed, capacity, latency, and reliability of message transmission under a wide variety of conditions. Functionality tests are a series of tests and code inspections that demonstrate the functionality of the particular subset of ISO protocols which have been implemented in a given NIU. Conformance tests are a series of tests which would expose whether or not selected features within the ISO protocols are present and interoperable.

Development of task network models of human performance in microgravity

NASA Technical Reports Server (NTRS)

Diaz, Manuel F.; Adam, Susan

1992-01-01

This paper discusses the utility of task-network modeling for quantifying human performance variability in microgravity. The data are gathered for: (1) improving current methodologies for assessing human performance and workload in the operational space environment; (2) developing tools for assessing alternative system designs; and (3) developing an integrated set of methodologies for the evaluation of performance degradation during extended duration spaceflight. The evaluation entailed an analysis of the Remote Manipulator System payload-grapple task performed on many shuttle missions. Task-network modeling can be used as a tool for assessing and enhancing human performance in man-machine systems, particularly for modeling long-duration manned spaceflight. Task-network modeling can be directed toward improving system efficiency by increasing the understanding of basic capabilities of the human component in the system and the factors that influence these capabilities.

Modeling and Performance Evaluation of Backoff Misbehaving Nodes in CSMA/CA Networks

DTIC Science & Technology

2012-08-01

Modeling and Performance Evaluation of Backoff Misbehaving Nodes in CSMA/CA Networks Zhuo Lu, Student Member, IEEE, Wenye Wang, Senior Member, IEEE... misbehaving nodes can obtain, we define and study two general classes of backoff misbehavior: continuous misbehavior, which keeps manipulating the backoff...misbehavior sporadically. Our approach is to introduce a new performance metric, namely order gain, to characterize the performance benefits of misbehaving

Metrics for evaluating performance and uncertainty of Bayesian network models

Treesearch

Bruce G. Marcot

2012-01-01

This paper presents a selected set of existing and new metrics for gauging Bayesian network model performance and uncertainty. Selected existing and new metrics are discussed for conducting model sensitivity analysis (variance reduction, entropy reduction, case file simulation); evaluating scenarios (influence analysis); depicting model complexity (numbers of model...

Quality of Recovery Evaluation of the Protection Schemes for Fiber-Wireless Access Networks

NASA Astrophysics Data System (ADS)

Fu, Minglei; Chai, Zhicheng; Le, Zichun

2016-03-01

With the rapid development of fiber-wireless (FiWi) access network, the protection schemes have got more and more attention due to the risk of huge data loss when failures occur. However, there are few studies on the performance evaluation of the FiWi protection schemes by the unified evaluation criterion. In this paper, quality of recovery (QoR) method was adopted to evaluate the performance of three typical protection schemes (MPMC scheme, OBOF scheme and RPMF scheme) against the segment-level failure in FiWi access network. The QoR models of the three schemes were derived in terms of availability, quality of backup path, recovery time and redundancy. To compare the performance of the three protection schemes comprehensively, five different classes of network services such as emergency service, prioritized elastic service, conversational service, etc. were utilized by means of assigning different QoR weights. Simulation results showed that, for the most service cases, RPMF scheme was proved to be the best solution to enhance the survivability when planning the FiWi access network.

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

Configuration development for ROMENET

NASA Astrophysics Data System (ADS)

Rhue, Lawrence

1989-10-01

A plan prepared by RJO Enterprises and BBN Communications Corporation (BBNCC) for the design of ROMENET, a DDN-like testbed for the Rome Air Development Center (RADC) Wide Area Networks (WAN) laboratory is presented. The ROMENET is intended to provide RADC with the ability to test and evaluate the performance and vulnerability of the Defense Data Network (DDN) technologies in support of specific Major Command programs and activities at RADC. It will also support experimentation with packet switched network technologies and includes facilities to analytically evaluate the performance of the network and its associated equipment and media. In addition, ROMENET will provide a simulation vehicle for controlled interference or jamming into the media for vulnerability assessment. Through interfaces with the RADC Battle Management Laboratory (BML), ROMENET will allow the Air Force to assess the restorative and performance characteristics of the network under stressed conditions. The closed environment of ROMENET makes it ideal for creating and testing routing algorithms and network control protocols.

Performance Evaluation of Communication Software Systems for Distributed Computing

NASA Technical Reports Server (NTRS)

Fatoohi, Rod

1996-01-01

In recent years there has been an increasing interest in object-oriented distributed computing since it is better quipped to deal with complex systems while providing extensibility, maintainability, and reusability. At the same time, several new high-speed network technologies have emerged for local and wide area networks. However, the performance of networking software is not improving as fast as the networking hardware and the workstation microprocessors. This paper gives an overview and evaluates the performance of the Common Object Request Broker Architecture (CORBA) standard in a distributed computing environment at NASA Ames Research Center. The environment consists of two testbeds of SGI workstations connected by four networks: Ethernet, FDDI, HiPPI, and ATM. The performance results for three communication software systems are presented, analyzed and compared. These systems are: BSD socket programming interface, IONA's Orbix, an implementation of the CORBA specification, and the PVM message passing library. The results show that high-level communication interfaces, such as CORBA and PVM, can achieve reasonable performance under certain conditions.

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.

OpenSHMEM-UCX : Evaluation of UCX for implementing OpenSHMEM Programming Model

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

Baker, Matthew B; Gorentla Venkata, Manjunath; Aderholdt, William Ferrol

2016-01-01

The OpenSHMEM reference implementation was developed towards the goal of developing an open source and high-performing Open- SHMEM implementation. To achieve portability and performance across various networks, the OpenSHMEM reference implementation uses GAS- Net and UCCS for network operations. Recently, new network layers have emerged with the promise of providing high-performance, scalabil- ity, and portability for HPC applications. In this paper, we implement the OpenSHMEM reference implementation to use the UCX framework for network operations. Then, we evaluate its performance and scalabil- ity on Cray XK systems to understand UCX s suitability for developing the OpenSHMEM programming model. Further, wemore » develop a bench- mark called SHOMS for evaluating the OpenSHMEM implementation. Our experimental results show that OpenSHMEM-UCX outperforms the vendor supplied OpenSHMEM implementation in most cases on the Cray XK system by up to 40% with respect to message rate and up to 70% for the execution of application kernels.« less

Challenges to Recruiting Population Representative Samples of Female Sex Workers in China Using Respondent Driven Sampling1

PubMed Central

Merli, M. Giovanna; Moody, James; Smith, Jeffrey; Li, Jing; Weir, Sharon; Chen, Xiangsheng

2014-01-01

We explore the network coverage of a sample of female sex workers (FSWs) in China recruited through Respondent Drive Sampling (RDS) as part of an effort to evaluate the claim of RDS of population representation with empirical data. We take advantage of unique information on the social networks of FSWs obtained from two overlapping studies --RDS and a venue-based sampling approach (PLACE) -- and use an exponential random graph modeling (ERGM) framework from local networks to construct a likely network from which our observed RDS sample is drawn. We then run recruitment chains over this simulated network to assess the assumption that the RDS chain referral process samples participants in proportion to their degree and the extent to which RDS satisfactorily covers certain parts of the network. We find evidence that, contrary to assumptions, RDS oversamples low degree nodes and geographically central areas of the network. Unlike previous evaluations of RDS which have explored the performance of RDS sampling chains on a non-hidden population, or the performance of simulated chains over previously mapped realistic social networks, our study provides a robust, empirically grounded evaluation of the performance of RDS chains on a real-world hidden population. PMID:24834869

Modelling and temporal performances evaluation of networked control systems using (max, +) algebra

NASA Astrophysics Data System (ADS)

Ammour, R.; Amari, S.

2015-01-01

In this paper, we address the problem of temporal performances evaluation of producer/consumer networked control systems. The aim is to develop a formal method for evaluating the response time of this type of control systems. Our approach consists on modelling, using Petri nets classes, the behaviour of the whole architecture including the switches that support multicast communications used by this protocol. (max, +) algebra formalism is then exploited to obtain analytical formulas of the response time and the maximal and minimal bounds. The main novelty is that our approach takes into account all delays experienced at the different stages of networked automation systems. Finally, we show how to apply the obtained results through an example of networked control system.

Using Neural Networks to Predict MBA Student Success

ERIC Educational Resources Information Center

Naik, Bijayananda; Ragothaman, Srinivasan

2004-01-01

Predicting MBA student performance for admission decisions is crucial for educational institutions. This paper evaluates the ability of three different models--neural networks, logit, and probit to predict MBA student performance in graduate programs. The neural network technique was used to classify applicants into successful and marginal student…

Assessing Advanced Technology in CENATE

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

Tallent, Nathan R.; Barker, Kevin J.; Gioiosa, Roberto

PNNL's Center for Advanced Technology Evaluation (CENATE) is a new U.S. Department of Energy center whose mission is to assess and facilitate access to emerging computing technology. CENATE is assessing a range of advanced technologies, from evolutionary to disruptive. Technologies of interest include the processor socket (homogeneous and accelerated systems), memories (dynamic, static, memory cubes), motherboards, networks (network interface cards and switches), and input/output and storage devices. CENATE is developing a multi-perspective evaluation process based on integrating advanced system instrumentation, performance measurements, and modeling and simulation. We show evaluations of two emerging network technologies: silicon photonics interconnects and the Datamore » Vortex network. CENATE's evaluation also addresses the question of which machine is best for a given workload under certain constraints. We show a performance-power tradeoff analysis of a well-known machine learning application on two systems.« less

Biological network motif detection and evaluation

PubMed Central

2011-01-01

Background Molecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as over-represented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves computationally challenging processes, previous algorithms have focused on computational efficiency. However, we believe that the biological quality of network motifs is also very important. Results We define biological network motifs as biologically significant subgraphs and traditional network motifs are differentiated as structural network motifs in this paper. We develop five algorithms, namely, EDGEGO-BNM, EDGEBETWEENNESS-BNM, NMF-BNM, NMFGO-BNM and VOLTAGE-BNM, for efficient detection of biological network motifs, and introduce several evaluation measures including motifs included in complex, motifs included in functional module and GO term clustering score in this paper. Experimental results show that EDGEGO-BNM and EDGEBETWEENNESS-BNM perform better than existing algorithms and all of our algorithms are applicable to find structural network motifs as well. Conclusion We provide new approaches to finding network motifs in biological networks. Our algorithms efficiently detect biological network motifs and further improve existing algorithms to find high quality structural network motifs, which would be impossible using existing algorithms. The performances of the algorithms are compared based on our new evaluation measures in biological contexts. We believe that our work gives some guidelines of network motifs research for the biological networks. PMID:22784624

MACHETE: Environment for Space Networking Evaluation

NASA Technical Reports Server (NTRS)

Jennings, Esther H.; Segui, John S.; Woo, Simon

2010-01-01

Space Exploration missions requires the design and implementation of space networking that differs from terrestrial networks. In a space networking architecture, interplanetary communication protocols need to be designed, validated and evaluated carefully to support different mission requirements. As actual systems are expensive to build, it is essential to have a low cost method to validate and verify mission/system designs and operations. This can be accomplished through simulation. Simulation can aid design decisions where alternative solutions are being considered, support trade-studies and enable fast study of what-if scenarios. It can be used to identify risks, verify system performance against requirements, and as an initial test environment as one moves towards emulation and actual hardware implementation of the systems. We describe the development of Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) and its use cases in supporting architecture trade studies, protocol performance and its role in hybrid simulation/emulation. The MACHETE environment contains various tools and interfaces such that users may select the set of tools tailored for the specific simulation end goal. The use cases illustrate tool combinations for simulating space networking in different mission scenarios. This simulation environment is useful in supporting space networking design for planned and future missions as well as evaluating performance of existing networks where non-determinism exist in data traffic and/or link conditions.

Experience with low-cost telemedicine in three different settings. Recommendations based on a proposed framework for network performance evaluation.

PubMed

Wootton, Richard; Vladzymyrskyy, Anton; Zolfo, Maria; Bonnardot, Laurent

2011-01-01

Telemedicine has been used for many years to support doctors in the developing world. Several networks provide services in different settings and in different ways. However, to draw conclusions about which telemedicine networks are successful requires a method of evaluating them. No general consensus or validated framework exists for this purpose. To define a basic method of performance measurement that can be used to improve and compare teleconsultation networks; to employ the proposed framework in an evaluation of three existing networks; to make recommendations about the future implementation and follow-up of such networks. Analysis based on the experience of three telemedicine networks (in operation for 7-10 years) that provide services to doctors in low-resource settings and which employ the same basic design. Although there are many possible indicators and metrics that might be relevant, five measures for each of the three user groups appear to be sufficient for the proposed framework. In addition, from the societal perspective, information about clinical- and cost-effectiveness is also required. The proposed performance measurement framework was applied to three mature telemedicine networks. Despite their differences in terms of activity, size and objectives, their performance in certain respects is very similar. For example, the time to first reply from an expert is about 24 hours for each network. Although all three networks had systems in place to collect data from the user perspective, none of them collected information about the coordinator's time required or about ease of system usage. They had only limited information about quality and cost. Measuring the performance of a telemedicine network is essential in understanding whether the network is working as intended and what effect it is having. Based on long-term field experience, the suggested framework is a practical tool that will permit organisations to assess the performance of their own networks and to improve them by comparison with others. All telemedicine systems should provide information about setup and running costs because cost-effectiveness is crucial for sustainability.

Experience with low-cost telemedicine in three different settings. Recommendations based on a proposed framework for network performance evaluation

PubMed Central

Wootton, Richard; Vladzymyrskyy, Anton; Zolfo, Maria; Bonnardot, Laurent

2011-01-01

Background Telemedicine has been used for many years to support doctors in the developing world. Several networks provide services in different settings and in different ways. However, to draw conclusions about which telemedicine networks are successful requires a method of evaluating them. No general consensus or validated framework exists for this purpose. Objective To define a basic method of performance measurement that can be used to improve and compare teleconsultation networks; to employ the proposed framework in an evaluation of three existing networks; to make recommendations about the future implementation and follow-up of such networks. Methods Analysis based on the experience of three telemedicine networks (in operation for 7–10 years) that provide services to doctors in low-resource settings and which employ the same basic design. Findings Although there are many possible indicators and metrics that might be relevant, five measures for each of the three user groups appear to be sufficient for the proposed framework. In addition, from the societal perspective, information about clinical- and cost-effectiveness is also required. The proposed performance measurement framework was applied to three mature telemedicine networks. Despite their differences in terms of activity, size and objectives, their performance in certain respects is very similar. For example, the time to first reply from an expert is about 24 hours for each network. Although all three networks had systems in place to collect data from the user perspective, none of them collected information about the coordinator's time required or about ease of system usage. They had only limited information about quality and cost. Conclusion Measuring the performance of a telemedicine network is essential in understanding whether the network is working as intended and what effect it is having. Based on long-term field experience, the suggested framework is a practical tool that will permit organisations to assess the performance of their own networks and to improve them by comparison with others. All telemedicine systems should provide information about setup and running costs because cost-effectiveness is crucial for sustainability. PMID:22162965

Performance Evaluation of 14 Neural Network Architectures Used for Predicting Heat Transfer Characteristics of Engine Oils

NASA Astrophysics Data System (ADS)

Al-Ajmi, R. M.; Abou-Ziyan, H. Z.; Mahmoud, M. A.

2012-01-01

This paper reports the results of a comprehensive study that aimed at identifying best neural network architecture and parameters to predict subcooled boiling characteristics of engine oils. A total of 57 different neural networks (NNs) that were derived from 14 different NN architectures were evaluated for four different prediction cases. The NNs were trained on experimental datasets performed on five engine oils of different chemical compositions. The performance of each NN was evaluated using a rigorous statistical analysis as well as careful examination of smoothness of predicted boiling curves. One NN, out of the 57 evaluated, correctly predicted the boiling curves for all cases considered either for individual oils or for all oils taken together. It was found that the pattern selection and weight update techniques strongly affect the performance of the NNs. It was also revealed that the use of descriptive statistical analysis such as R2, mean error, standard deviation, and T and slope tests, is a necessary but not sufficient condition for evaluating NN performance. The performance criteria should also include inspection of the smoothness of the predicted curves either visually or by plotting the slopes of these curves.

Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data.

PubMed

Chen, Shuonan; Mar, Jessica C

2018-06-19

A fundamental fact in biology states that genes do not operate in isolation, and yet, methods that infer regulatory networks for single cell gene expression data have been slow to emerge. With single cell sequencing methods now becoming accessible, general network inference algorithms that were initially developed for data collected from bulk samples may not be suitable for single cells. Meanwhile, although methods that are specific for single cell data are now emerging, whether they have improved performance over general methods is unknown. In this study, we evaluate the applicability of five general methods and three single cell methods for inferring gene regulatory networks from both experimental single cell gene expression data and in silico simulated data. Standard evaluation metrics using ROC curves and Precision-Recall curves against reference sets sourced from the literature demonstrated that most of the methods performed poorly when they were applied to either experimental single cell data, or simulated single cell data, which demonstrates their lack of performance for this task. Using default settings, network methods were applied to the same datasets. Comparisons of the learned networks highlighted the uniqueness of some predicted edges for each method. The fact that different methods infer networks that vary substantially reflects the underlying mathematical rationale and assumptions that distinguish network methods from each other. This study provides a comprehensive evaluation of network modeling algorithms applied to experimental single cell gene expression data and in silico simulated datasets where the network structure is known. Comparisons demonstrate that most of these assessed network methods are not able to predict network structures from single cell expression data accurately, even if they are specifically developed for single cell methods. Also, single cell methods, which usually depend on more elaborative algorithms, in general have less similarity to each other in the sets of edges detected. The results from this study emphasize the importance for developing more accurate optimized network modeling methods that are compatible for single cell data. Newly-developed single cell methods may uniquely capture particular features of potential gene-gene relationships, and caution should be taken when we interpret these results.

Performance evaluation of a burst-mode EDFA in an optical packet and circuit integrated network.

PubMed

Shiraiwa, Masaki; Awaji, Yoshinari; Furukawa, Hideaki; Shinada, Satoshi; Puttnam, Benjamin J; Wada, Naoya

2013-12-30

We experimentally investigate the performance of burst-mode EDFA in an optical packet and circuit integrated system. In such networks, packets and light paths can be dynamically assigned to the same fibers, resulting in gain transients in EDFAs throughout the network that can limit network performance. Here, we compare the performance of a 'burst-mode' EDFA (BM-EDFA), employing transient suppression techniques and optical feedback, with conventional EDFAs, and those using automatic gain control and previous BM-EDFA implementations. We first measure gain transients and other impairments in a simplified set-up before making frame error-rate measurements in a network demonstration.

Statistical performance evaluation of ECG transmission using wireless networks.

PubMed

Shakhatreh, Walid; Gharaibeh, Khaled; Al-Zaben, Awad

2013-07-01

This paper presents simulation of the transmission of biomedical signals (using ECG signal as an example) over wireless networks. Investigation of the effect of channel impairments including SNR, pathloss exponent, path delay and network impairments such as packet loss probability; on the diagnosability of the received ECG signal are presented. The ECG signal is transmitted through a wireless network system composed of two communication protocols; an 802.15.4- ZigBee protocol and an 802.11b protocol. The performance of the transmission is evaluated using higher order statistics parameters such as kurtosis and Negative Entropy in addition to the common techniques such as the PRD, RMS and Cross Correlation.

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.

Routing optimization in networks based on traffic gravitational field model

NASA Astrophysics Data System (ADS)

Liu, Longgeng; Luo, Guangchun

2017-04-01

For research on the gravitational field routing mechanism on complex networks, we further analyze the gravitational effect of paths. In this study, we introduce the concept of path confidence degree to evaluate the unblocked reliability of paths that it takes the traffic state of all nodes on the path into account from the overall. On the basis of this, we propose an improved gravitational field routing protocol considering all the nodes’ gravities on the path and the path confidence degree. In order to evaluate the transmission performance of the routing strategy, an order parameter is introduced to measure the network throughput by the critical value of phase transition from a free-flow phase to a jammed phase, and the betweenness centrality is used to evaluate the transmission performance and traffic congestion of the network. Simulation results show that compared with the shortest-path routing strategy and the previous gravitational field routing strategy, the proposed algorithm improves the network throughput considerably and effectively balances the traffic load within the network, and all nodes in the network are utilized high efficiently. As long as γ ≥ α, the transmission performance can reach the maximum and remains unchanged for different α and γ, which ensures that the proposed routing protocol is high efficient and stable.

Performance evaluation of multi-stratum resources optimization with network functions virtualization for cloud-based radio over optical fiber networks.

PubMed

Yang, Hui; He, Yongqi; Zhang, Jie; Ji, Yuefeng; Bai, Wei; Lee, Young

2016-04-18

Cloud radio access network (C-RAN) has become a promising scenario to accommodate high-performance services with ubiquitous user coverage and real-time cloud computing using cloud BBUs. In our previous work, we implemented cross stratum optimization of optical network and application stratums resources that allows to accommodate the services in optical networks. In view of this, this study extends to consider the multiple dimensional resources optimization of radio, optical and BBU processing in 5G age. We propose a novel multi-stratum resources optimization (MSRO) architecture with network functions virtualization for cloud-based radio over optical fiber networks (C-RoFN) using software defined control. A global evaluation scheme (GES) for MSRO in C-RoFN is introduced based on the proposed architecture. The MSRO can enhance the responsiveness to dynamic end-to-end user demands and globally optimize radio frequency, optical and BBU resources effectively to maximize radio coverage. The efficiency and feasibility of the proposed architecture are experimentally demonstrated on OpenFlow-based enhanced SDN testbed. The performance of GES under heavy traffic load scenario is also quantitatively evaluated based on MSRO architecture in terms of resource occupation rate and path provisioning latency, compared with other provisioning scheme.

Flow-oriented dynamic assembly algorithm in TCP over OBS networks

NASA Astrophysics Data System (ADS)

Peng, Shuping; Li, Zhengbin; He, Yongqi; Xu, Anshi

2008-11-01

OBS is envisioned as a promising infrastructure for the next generation optical network, and TCP is likely to be the dominant transport protocol in the next generation network. Therefore, it is necessary to evaluate the performance of TCP over OBS networks. The assembly at the ingress edge nodes will impact the network performance. There have been several Fixed Assembly Period (FAP) algorithms proposed. However, the assembly period in FAP is fixed, and it can not be adjusted according to the network condition. Moreover, in FAP, the packets from different TCP sources are assembled into one burst. In that case, if such a burst is dropped, the TCP windows of the corresponding sources will shrink and the throughput will be reduced. In this paper, we introduced a flow-oriented Dynamic Assembly Period (DAP) algorithm for TCP over OBS networks. Through comparing the previous and current burst lengths, DAP can track the variation of TCP window, and update the assembly period dynamically for the next assembly. The performance of DAP is evaluated over a single TCP connection and multiple connections, respectively. The simulation results show that DAP performs better than FAP at almost the whole range of burst dropping probability.

NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference.

PubMed

Bellot, Pau; Olsen, Catharina; Salembier, Philippe; Oliveras-Vergés, Albert; Meyer, Patrick E

2015-09-29

In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods. Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities. The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.

Holographic neural networks versus conventional neural networks: a comparative evaluation for the classification of landmine targets in ground-penetrating radar images

NASA Astrophysics Data System (ADS)

Mudigonda, Naga R.; Kacelenga, Ray; Edwards, Mark

2004-09-01

This paper evaluates the performance of a holographic neural network in comparison with a conventional feedforward backpropagation neural network for the classification of landmine targets in ground penetrating radar images. The data used in the study was acquired from four different test sites using the landmine detection system developed by General Dynamics Canada Ltd., in collaboration with the Defense Research and Development Canada, Suffield. A set of seven features extracted for each detected alarm is used as stimulus inputs for the networks. The recall responses of the networks are then evaluated against the ground truth to declare true or false detections. The area computed under the receiver operating characteristic curve is used for comparative purposes. With a large dataset comprising of data from multiple sites, both the holographic and conventional networks showed comparable trends in recall accuracies with area values of 0.88 and 0.87, respectively. By using independent validation datasets, the holographic network"s generalization performance was observed to be better (mean area = 0.86) as compared to the conventional network (mean area = 0.82). Despite the widely publicized theoretical advantages of the holographic technology, use of more than the required number of cortical memory elements resulted in an over-fitting phenomenon of the holographic network.

Analysis of 100Mb/s Ethernet for the Whitney Commodity Computing Testbed

NASA Technical Reports Server (NTRS)

Fineberg, Samuel A.; Pedretti, Kevin T.; Kutler, Paul (Technical Monitor)

1997-01-01

We evaluate the performance of a Fast Ethernet network configured with a single large switch, a single hub, and a 4x4 2D torus topology in a testbed cluster of "commodity" Pentium Pro PCs. We also evaluated a mixed network composed of ethernet hubs and switches. An MPI collective communication benchmark, and the NAS Parallel Benchmarks version 2.2 (NPB2) show that the torus network performs best for all sizes that we were able to test (up to 16 nodes). For larger networks the ethernet switch outperforms the hub, though its performance is far less than peak. The hub/switch combination tests indicate that the NAS parallel benchmarks are relatively insensitive to hub densities of less than 7 nodes per hub.

Diagnosis of helicopter gearboxes using structure-based networks

NASA Technical Reports Server (NTRS)

Jammu, Vinay B.; Danai, Kourosh; Lewicki, David G.

1995-01-01

A connectionist network is introduced for fault diagnosis of helicopter gearboxes that incorporates knowledge of the gearbox structure and characteristics of the vibration features as its fuzzy weights. Diagnosis is performed by propagating the abnormal features of vibration measurements through this Structure-Based Connectionist Network (SBCN), the outputs of which represent the fault possibility values for individual components of the gearbox. The performance of this network is evaluated by applying it to experimental vibration data from an OH-58A helicopter gearbox. The diagnostic results indicate that the network performance is comparable to those obtained from supervised pattern classification.

ICC '86; Proceedings of the International Conference on Communications, Toronto, Canada, June 22-25, 1986, Conference Record. Volumes 1, 2, & 3

NASA Astrophysics Data System (ADS)

Papers are presented on ISDN, mobile radio systems and techniques for digital connectivity, centralized and distributed algorithms in computer networks, communications networks, quality assurance and impact on cost, adaptive filters in communications, the spread spectrum, signal processing, video communication techniques, and digital satellite services. Topics discussed include performance evaluation issues for integrated protocols, packet network operations, the computer network theory and multiple-access, microwave single sideband systems, switching architectures, fiber optic systems, wireless local communications, modulation, coding, and synchronization, remote switching, software quality, transmission, and expert systems in network operations. Consideration is given to wide area networks, image and speech processing, office communications application protocols, multimedia systems, customer-controlled network operations, digital radio systems, channel modeling and signal processing in digital communications, earth station/on-board modems, computer communications system performance evaluation, source encoding, compression, and quantization, and adaptive communications systems.

Advice networks in teams: the role of transformational leadership and members' core self-evaluations.

PubMed

Zhang, Zhen; Peterson, Suzanne J

2011-09-01

This article examines the team-level factors promoting advice exchange networks in teams. Drawing upon theory and research on transformational leadership, team diversity, and social networks, we hypothesized that transformational leadership positively influences advice network density in teams and that advice network density serves as a mediating mechanism linking transformational leadership to team performance. We further hypothesized a 3-way interaction in which members' mean core self-evaluation (CSE) and diversity in CSE jointly moderate the transformational leadership-advice network density relationship, such that the relationship is positive and stronger for teams with low diversity in CSE and high mean CSE. In addition, we expected that advice network centralization attenuates the positive influence of network density on team performance. Results based on multisource data from 79 business unit management teams showed support for these hypotheses. The results highlight the pivotal role played by transformational leadership and team members' CSEs in enhancing team social networks and, ultimately, team effectiveness. PsycINFO Database Record (c) 2011 APA, all rights reserved

Information Assurance Tasks Supporting the Processing of Electronic Records Archives

DTIC Science & Technology

2007-03-01

3 Table 2. OpenVPN evaluation results...........................................................................................10 iv 1...operation of necessary security features and compare the network performance under OpenVPN (openvpn.net) operation with the network performance under no...VPN operation (non-VPN) in a gigabit network environment. The reason for selecting OpenVPN product was based on the previous findings of Khanvilkar

Simulation and modeling of the temporal performance of path-based restoration schemes in planar mesh networks

NASA Astrophysics Data System (ADS)

Bhardwaj, Manish; McCaughan, Leon; Olkhovets, Anatoli; Korotky, Steven K.

2006-12-01

We formulate an analytic framework for the restoration performance of path-based restoration schemes in planar mesh networks. We analyze various switch architectures and signaling schemes and model their total restoration interval. We also evaluate the network global expectation value of the time to restore a demand as a function of network parameters. We analyze a wide range of nominally capacity-optimal planar mesh networks and find our analytic model to be in good agreement with numerical simulation data.

Building and measuring a high performance network architecture

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

Kramer, William T.C.; Toole, Timothy; Fisher, Chuck

2001-04-20

Once a year, the SC conferences present a unique opportunity to create and build one of the most complex and highest performance networks in the world. At SC2000, large-scale and complex local and wide area networking connections were demonstrated, including large-scale distributed applications running on different architectures. This project was designed to use the unique opportunity presented at SC2000 to create a testbed network environment and then use that network to demonstrate and evaluate high performance computational and communication applications. This testbed was designed to incorporate many interoperable systems and services and was designed for measurement from the very beginning.more » The end results were key insights into how to use novel, high performance networking technologies and to accumulate measurements that will give insights into the networks of the future.« less

Evaluating the effect of street network connectivity on first/last mile transit performance.

DOT National Transportation Integrated Search

2011-11-01

"This study defines a novel connectivity indicator (CI) to predict transit performance by : identifying the role that street network connectivity plays in influencing the service quality of : demand responsive feeder transit services. This new CI def...

Neural network-based model reference adaptive control system.

PubMed

Patino, H D; Liu, D

2000-01-01

In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

Using hybrid method to evaluate the green performance in uncertainty.

PubMed

Tseng, Ming-Lang; Lan, Lawrence W; Wang, Ray; Chiu, Anthony; Cheng, Hui-Ping

2011-04-01

Green performance measure is vital for enterprises in making continuous improvements to maintain sustainable competitive advantages. Evaluation of green performance, however, is a challenging task due to the dependence complexity of the aspects, criteria, and the linguistic vagueness of some qualitative information and quantitative data together. To deal with this issue, this study proposes a novel approach to evaluate the dependence aspects and criteria of firm's green performance. The rationale of the proposed approach, namely green network balanced scorecard, is using balanced scorecard to combine fuzzy set theory with analytical network process (ANP) and importance-performance analysis (IPA) methods, wherein fuzzy set theory accounts for the linguistic vagueness of qualitative criteria and ANP converts the relations among the dependence aspects and criteria into an intelligible structural modeling used IPA. For the empirical case study, four dependence aspects and 34 green performance criteria for PCB firms in Taiwan were evaluated. The managerial implications are discussed.

Statistical Classification for Cognitive Diagnostic Assessment: An Artificial Neural Network Approach

ERIC Educational Resources Information Center

Cui, Ying; Gierl, Mark; Guo, Qi

2016-01-01

The purpose of the current investigation was to describe how the artificial neural networks (ANNs) can be used to interpret student performance on cognitive diagnostic assessments (CDAs) and evaluate the performances of ANNs using simulation results. CDAs are designed to measure student performance on problem-solving tasks and provide useful…

Cortical connectivity and memory performance in cognitive decline: A study via graph theory from EEG data.

PubMed

Vecchio, F; Miraglia, F; Quaranta, D; Granata, G; Romanello, R; Marra, C; Bramanti, P; Rossini, P M

2016-03-01

Functional brain abnormalities including memory loss are found to be associated with pathological changes in connectivity and network neural structures. Alzheimer's disease (AD) interferes with memory formation from the molecular level, to synaptic functions and neural networks organization. Here, we determined whether brain connectivity of resting-state networks correlate with memory in patients affected by AD and in subjects with mild cognitive impairment (MCI). One hundred and forty-four subjects were recruited: 70 AD (MMSE Mini Mental State Evaluation 21.4), 50 MCI (MMSE 25.2) and 24 healthy subjects (MMSE 29.8). Undirected and weighted cortical brain network was built to evaluate graph core measures to obtain Small World parameters. eLORETA lagged linear connectivity as extracted by electroencephalogram (EEG) signals was used to weight the network. A high statistical correlation between Small World and memory performance was found. Namely, higher Small World characteristic in EEG gamma frequency band during the resting state, better performance in short-term memory as evaluated by the digit span tests. Such Small World pattern might represent a biomarker of working memory impairment in older people both in physiological and pathological conditions. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

Performance analysis of wireless sensor networks in geophysical sensing applications

NASA Astrophysics Data System (ADS)

Uligere Narasimhamurthy, Adithya

Performance is an important criteria to consider before switching from a wired network to a wireless sensing network. Performance is especially important in geophysical sensing where the quality of the sensing system is measured by the precision of the acquired signal. Can a wireless sensing network maintain the same reliability and quality metrics that a wired system provides? Our work focuses on evaluating the wireless GeoMote sensor motes that were developed by previous computer science graduate students at Mines. Specifically, we conducted a set of experiments, namely WalkAway and Linear Array experiments, to characterize the performance of the wireless motes. The motes were also equipped with the Sticking Heartbeat Aperture Resynchronization Protocol (SHARP), a time synchronization protocol developed by a previous computer science graduate student at Mines. This protocol should automatically synchronize the mote's internal clocks and reduce time synchronization errors. We also collected passive data to evaluate the response of GeoMotes to various frequency components associated with the seismic waves. With the data collected from these experiments, we evaluated the performance of the SHARP protocol and compared the performance of our GeoMote wireless system against the industry standard wired seismograph system (Geometric-Geode). Using arrival time analysis and seismic velocity calculations, we set out to answer the following question. Can our wireless sensing system (GeoMotes) perform similarly to a traditional wired system in a realistic scenario?

Performance Evaluation in Network-Based Parallel Computing

NASA Technical Reports Server (NTRS)

Dezhgosha, Kamyar

1996-01-01

Network-based parallel computing is emerging as a cost-effective alternative for solving many problems which require use of supercomputers or massively parallel computers. The primary objective of this project has been to conduct experimental research on performance evaluation for clustered parallel computing. First, a testbed was established by augmenting our existing SUNSPARCs' network with PVM (Parallel Virtual Machine) which is a software system for linking clusters of machines. Second, a set of three basic applications were selected. The applications consist of a parallel search, a parallel sort, a parallel matrix multiplication. These application programs were implemented in C programming language under PVM. Third, we conducted performance evaluation under various configurations and problem sizes. Alternative parallel computing models and workload allocations for application programs were explored. The performance metric was limited to elapsed time or response time which in the context of parallel computing can be expressed in terms of speedup. The results reveal that the overhead of communication latency between processes in many cases is the restricting factor to performance. That is, coarse-grain parallelism which requires less frequent communication between processes will result in higher performance in network-based computing. Finally, we are in the final stages of installing an Asynchronous Transfer Mode (ATM) switch and four ATM interfaces (each 155 Mbps) which will allow us to extend our study to newer applications, performance metrics, and configurations.

Study on automatic ECT data evaluation by using neural network

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

Komatsu, H.; Matsumoto, Y.; Badics, Z.

1994-12-31

At the in--service inspection of the steam generator (SG) tubings in Pressurized Water Reactor (PWR) plant, eddy current testing (ECT) has been widely used at each outage. At present, ECT data evaluation is mainly performed by ECT data analyst, therefore it has the following problems. Only ECT signal configuration on the impedance trajectory is used in the evaluation. It is an enormous time consuming process. The evaluation result is influenced by the ability and experience of the analyst. Especially, it is difficult to identify the true defect signal hidden in background signals such as lift--off noise and deposit signals. Inmore » this work, the authors performed the study on the possibility of the application of neural network to ECT data evaluation. It was demonstrated that the neural network proved to be effective to identify the nature of defect, by selecting several optimum input parameters to categorize the raw ECT signals.« less

A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals.

PubMed

Tsiouris, Κostas Μ; Pezoulas, Vasileios C; Zervakis, Michalis; Konitsiotis, Spiros; Koutsouris, Dimitrios D; Fotiadis, Dimitrios I

2018-05-17

The electroencephalogram (EEG) is the most prominent means to study epilepsy and capture changes in electrical brain activity that could declare an imminent seizure. In this work, Long Short-Term Memory (LSTM) networks are introduced in epileptic seizure prediction using EEG signals, expanding the use of deep learning algorithms with convolutional neural networks (CNN). A pre-analysis is initially performed to find the optimal architecture of the LSTM network by testing several modules and layers of memory units. Based on these results, a two-layer LSTM network is selected to evaluate seizure prediction performance using four different lengths of preictal windows, ranging from 15 min to 2 h. The LSTM model exploits a wide range of features extracted prior to classification, including time and frequency domain features, between EEG channels cross-correlation and graph theoretic features. The evaluation is performed using long-term EEG recordings from the open CHB-MIT Scalp EEG database, suggest that the proposed methodology is able to predict all 185 seizures, providing high rates of seizure prediction sensitivity and low false prediction rates (FPR) of 0.11-0.02 false alarms per hour, depending on the duration of the preictal window. The proposed LSTM-based methodology delivers a significant increase in seizure prediction performance compared to both traditional machine learning techniques and convolutional neural networks that have been previously evaluated in the literature. Copyright © 2018 Elsevier Ltd. All rights reserved.

Analysis of metro network performance from a complex network perspective

NASA Astrophysics Data System (ADS)

Wu, Xingtang; Dong, Hairong; Tse, Chi Kong; Ho, Ivan W. H.; Lau, Francis C. M.

2018-02-01

In this paper, the performance of metro networks is studied from a network science perspective. We review the structural efficiency of metro networks on the basis of a passenger's intuitive routing strategy that optimizes the number of transfers and the distance traveled.A new node centrality measure, called node occupying probability, is introduced for evaluating the level of utilization of stations. The robustness of a metro network is analyzed under several attack scenarios. Six metro networks (Beijing, London, Paris, Hong Kong, Tokyo and New York) are compared in terms of the node occupying probability and a few other performance parameters. Simulation results show that the New York metro system has better topological efficiency, the Tokyo and Hong Kong systems are the most robust under random attack and target attack, respectively.

Frequency Assignment for Joint Aerial Layer Network High-Capacity Backbone

DTIC Science & Technology

2017-08-11

Department of the Army position unless so designated by other authorized documents. Citation of manufacturer’s or trade names does not constitute an...performance of the proposed approach. Frequency Assignment, JALN, Resource Allocation, Network Optimization, Performance Evaluation 24 Peng Wang 410-278

AIDS Clinical Trials Group Network

MedlinePlus

... ACTG (PDF - 42 KB) Bylaws, SOPs, and Guidelines Leadership and Operations Center Network Coordinating Center Statistical and Data Management Center Performance Evaluation Program Sites Community General Information ...

Reliability studies of Integrated Modular Engine system designs

NASA Technical Reports Server (NTRS)

Hardy, Terry L.; Rapp, Douglas C.

1993-01-01

A study was performed to evaluate the reliability of Integrated Modular Engine (IME) concepts. Comparisons were made between networked IME systems and non-networked discrete systems using expander cycle configurations. Both redundant and non-redundant systems were analyzed. Binomial approximation and Markov analysis techniques were employed to evaluate total system reliability. In addition, Failure Modes and Effects Analyses (FMEA), Preliminary Hazard Analyses (PHA), and Fault Tree Analysis (FTA) were performed to allow detailed evaluation of the IME concept. A discussion of these system reliability concepts is also presented.

Reliability studies of integrated modular engine system designs

NASA Technical Reports Server (NTRS)

Hardy, Terry L.; Rapp, Douglas C.

1993-01-01

A study was performed to evaluate the reliability of Integrated Modular Engine (IME) concepts. Comparisons were made between networked IME systems and non-networked discrete systems using expander cycle configurations. Both redundant and non-redundant systems were analyzed. Binomial approximation and Markov analysis techniques were employed to evaluate total system reliability. In addition, Failure Modes and Effects Analyses (FMEA), Preliminary Hazard Analyses (PHA), and Fault Tree Analysis (FTA) were performed to allow detailed evaluation of the IME concept. A discussion of these system reliability concepts is also presented.

Reliability studies of integrated modular engine system designs

NASA Astrophysics Data System (ADS)

Hardy, Terry L.; Rapp, Douglas C.

1993-06-01

A study was performed to evaluate the reliability of Integrated Modular Engine (IME) concepts. Comparisons were made between networked IME systems and non-networked discrete systems using expander cycle configurations. Both redundant and non-redundant systems were analyzed. Binomial approximation and Markov analysis techniques were employed to evaluate total system reliability. In addition, Failure Modes and Effects Analyses (FMEA), Preliminary Hazard Analyses (PHA), and Fault Tree Analysis (FTA) were performed to allow detailed evaluation of the IME concept. A discussion of these system reliability concepts is also presented.

Reliability studies of Integrated Modular Engine system designs

NASA Astrophysics Data System (ADS)

Hardy, Terry L.; Rapp, Douglas C.

1993-06-01

A study was performed to evaluate the reliability of Integrated Modular Engine (IME) concepts. Comparisons were made between networked IME systems and non-networked discrete systems using expander cycle configurations. Both redundant and non-redundant systems were analyzed. Binomial approximation and Markov analysis techniques were employed to evaluate total system reliability. In addition, Failure Modes and Effects Analyses (FMEA), Preliminary Hazard Analyses (PHA), and Fault Tree Analysis (FTA) were performed to allow detailed evaluation of the IME concept. A discussion of these system reliability concepts is also presented.

An investigation of networking techniques for the ASRM facility

NASA Technical Reports Server (NTRS)

Moorhead, Robert J., II; Smith, Wayne D.; Thompson, Dale R.

1992-01-01

This report is based on the early design concepts for a communications network for the Advanced Solid Rocket Motor (ASRM) facility being built at Yellow Creek near Iuka, MS. The investigators have participated in the early design concepts and in the evaluation of the initial concepts. The continuing system design effort and any modification of the plan will require a careful evaluation of the required bandwidth of the network, the capabilities of the protocol, and the requirements of the controllers and computers on the network. The overall network, which is heterogeneous in protocol and bandwidth, is being modeled, analyzed, simulated, and tested to obtain some degree of confidence in its performance capabilities and in its performance under nominal and heavy loads. The results of the proposed work should have an impact on the design and operation of the ASRM facility.

Neural network classification of clinical neurophysiological data for acute care monitoring

NASA Technical Reports Server (NTRS)

Sgro, Joseph

1994-01-01

The purpose of neurophysiological monitoring of the 'acute care' patient is to allow the accurate recognition of changing or deteriorating neurological function as close to the moment of occurrence as possible, thus permitting immediate intervention. Results confirm that: (1) neural networks are able to accurately identify electroencephalogram (EEG) patterns and evoked potential (EP) wave components, and measuring EP waveform latencies and amplitudes; (2) neural networks are able to accurately detect EP and EEG recordings that have been contaminated by noise; (3) the best performance was obtained consistently with the back propagation network for EP and the HONN for EEG's; (4) neural network performed consistently better than other methods evaluated; and (5) neural network EEG and EP analyses are readily performed on multichannel data.

Software-defined Radio Based Measurement Platform for Wireless Networks

PubMed Central

Chao, I-Chun; Lee, Kang B.; Candell, Richard; Proctor, Frederick; Shen, Chien-Chung; Lin, Shinn-Yan

2015-01-01

End-to-end latency is critical to many distributed applications and services that are based on computer networks. There has been a dramatic push to adopt wireless networking technologies and protocols (such as WiFi, ZigBee, WirelessHART, Bluetooth, ISA100.11a, etc.) into time-critical applications. Examples of such applications include industrial automation, telecommunications, power utility, and financial services. While performance measurement of wired networks has been extensively studied, measuring and quantifying the performance of wireless networks face new challenges and demand different approaches and techniques. In this paper, we describe the design of a measurement platform based on the technologies of software-defined radio (SDR) and IEEE 1588 Precision Time Protocol (PTP) for evaluating the performance of wireless networks. PMID:27891210

Software-defined Radio Based Measurement Platform for Wireless Networks.

PubMed

Chao, I-Chun; Lee, Kang B; Candell, Richard; Proctor, Frederick; Shen, Chien-Chung; Lin, Shinn-Yan

2015-10-01

End-to-end latency is critical to many distributed applications and services that are based on computer networks. There has been a dramatic push to adopt wireless networking technologies and protocols (such as WiFi, ZigBee, WirelessHART, Bluetooth, ISA100.11a, etc. ) into time-critical applications. Examples of such applications include industrial automation, telecommunications, power utility, and financial services. While performance measurement of wired networks has been extensively studied, measuring and quantifying the performance of wireless networks face new challenges and demand different approaches and techniques. In this paper, we describe the design of a measurement platform based on the technologies of software-defined radio (SDR) and IEEE 1588 Precision Time Protocol (PTP) for evaluating the performance of wireless networks.

MANEMO Routing in Practice: Protocol Selection, Expected Performance, and Experimental Evaluation

NASA Astrophysics Data System (ADS)

Tazaki, Hajime; van Meter, Rodney; Wakikawa, Ryuji; Wongsaardsakul, Thirapon; Kanchanasut, Kanchana; Dias de Amorim, Marcelo; Murai, Jun

Motivated by the deployment of post-disaster MANEMO (MANET for NEMO) composed of mobile routers and stations, we evaluate two candidate routing protocols through network simulation, theoretical performance analysis, and field experiments. The first protocol is the widely adopted Optimized Link State Routing protocol (OLSR) and the second is the combination of the Tree Discovery Protocol (TDP) with Network In Node Advertisement (NINA). To the best of our knowledge, this is the first time that these two protocols are compared in both theoretical and practical terms. We focus on the control overhead generated when mobile routers perform a handover. Our results confirm the correctness and operational robustness of both protocols. More interestingly, although in the general case OLSR leads to better results, TDP/NINA outperforms OLSR both in the case of sparse networks and in highly mobile networks, which correspond to the operation point of a large set of post-disaster scenarios.

Measuring Road Network Vulnerability with Sensitivity Analysis

PubMed Central

Jun-qiang, Leng; Long-hai, Yang; Liu, Wei-yi; Zhao, Lin

2017-01-01

This paper focuses on the development of a method for road network vulnerability analysis, from the perspective of capacity degradation, which seeks to identify the critical infrastructures in the road network and the operational performance of the whole traffic system. This research involves defining the traffic utility index and modeling vulnerability of road segment, route, OD (Origin Destination) pair and road network. Meanwhile, sensitivity analysis method is utilized to calculate the change of traffic utility index due to capacity degradation. This method, compared to traditional traffic assignment, can improve calculation efficiency and make the application of vulnerability analysis to large actual road network possible. Finally, all the above models and calculation method is applied to actual road network evaluation to verify its efficiency and utility. This approach can be used as a decision-supporting tool for evaluating the performance of road network and identifying critical infrastructures in transportation planning and management, especially in the resource allocation for mitigation and recovery. PMID:28125706

A comparative study of routing protocols of heterogeneous wireless sensor networks.

PubMed

Han, Guangjie; Jiang, Xu; Qian, Aihua; Rodrigues, Joel J P C; Cheng, Long

2014-01-01

Recently, heterogeneous wireless sensor network (HWSN) routing protocols have drawn more and more attention. Various HWSN routing protocols have been proposed to improve the performance of HWSNs. Among these protocols, hierarchical HWSN routing protocols can improve the performance of the network significantly. In this paper, we will evaluate three hierarchical HWSN protocols proposed recently--EDFCM, MCR, and EEPCA--together with two previous classical routing protocols--LEACH and SEP. We mainly focus on the round of the first node dies (also called the stable period) and the number of packets sent to sink, which is an important aspect to evaluate the monitoring ability of a protocol. We conduct a lot of experiments and simulations on Matlab to analyze the performance of the five routing protocols.

IndeCut evaluates performance of network motif discovery algorithms.

PubMed

Ansariola, Mitra; Megraw, Molly; Koslicki, David

2018-05-01

Genomic networks represent a complex map of molecular interactions which are descriptive of the biological processes occurring in living cells. Identifying the small over-represented circuitry patterns in these networks helps generate hypotheses about the functional basis of such complex processes. Network motif discovery is a systematic way of achieving this goal. However, a reliable network motif discovery outcome requires generating random background networks which are the result of a uniform and independent graph sampling method. To date, there has been no method to numerically evaluate whether any network motif discovery algorithm performs as intended on realistically sized datasets-thus it was not possible to assess the validity of resulting network motifs. In this work, we present IndeCut, the first method to date that characterizes network motif finding algorithm performance in terms of uniform sampling on realistically sized networks. We demonstrate that it is critical to use IndeCut prior to running any network motif finder for two reasons. First, IndeCut indicates the number of samples needed for a tool to produce an outcome that is both reproducible and accurate. Second, IndeCut allows users to choose the tool that generates samples in the most independent fashion for their network of interest among many available options. The open source software package is available at https://github.com/megrawlab/IndeCut. megrawm@science.oregonstate.edu or david.koslicki@math.oregonstate.edu. Supplementary data are available at Bioinformatics online.

The effect of a loss of model structural detail due to network skeletonization on contamination warning system design: case studies.

PubMed

Davis, Michael J; Janke, Robert

2018-01-04

The effect of limitations in the structural detail available in a network model on contamination warning system (CWS) design was examined in case studies using the original and skeletonized network models for two water distribution systems (WDSs). The skeletonized models were used as proxies for incomplete network models. CWS designs were developed by optimizing sensor placements for worst-case and mean-case contamination events. Designs developed using the skeletonized network models were transplanted into the original network model for evaluation. CWS performance was defined as the number of people who ingest more than some quantity of a contaminant in tap water before the CWS detects the presence of contamination. Lack of structural detail in a network model can result in CWS designs that (1) provide considerably less protection against worst-case contamination events than that obtained when a more complete network model is available and (2) yield substantial underestimates of the consequences associated with a contamination event. Nevertheless, CWSs developed using skeletonized network models can provide useful reductions in consequences for contaminants whose effects are not localized near the injection location. Mean-case designs can yield worst-case performances similar to those for worst-case designs when there is uncertainty in the network model. Improvements in network models for WDSs have the potential to yield significant improvements in CWS designs as well as more realistic evaluations of those designs. Although such improvements would be expected to yield improved CWS performance, the expected improvements in CWS performance have not been quantified previously. The results presented here should be useful to those responsible for the design or implementation of CWSs, particularly managers and engineers in water utilities, and encourage the development of improved network models.

The effect of a loss of model structural detail due to network skeletonization on contamination warning system design: case studies

NASA Astrophysics Data System (ADS)

Davis, Michael J.; Janke, Robert

2018-05-01

The effect of limitations in the structural detail available in a network model on contamination warning system (CWS) design was examined in case studies using the original and skeletonized network models for two water distribution systems (WDSs). The skeletonized models were used as proxies for incomplete network models. CWS designs were developed by optimizing sensor placements for worst-case and mean-case contamination events. Designs developed using the skeletonized network models were transplanted into the original network model for evaluation. CWS performance was defined as the number of people who ingest more than some quantity of a contaminant in tap water before the CWS detects the presence of contamination. Lack of structural detail in a network model can result in CWS designs that (1) provide considerably less protection against worst-case contamination events than that obtained when a more complete network model is available and (2) yield substantial underestimates of the consequences associated with a contamination event. Nevertheless, CWSs developed using skeletonized network models can provide useful reductions in consequences for contaminants whose effects are not localized near the injection location. Mean-case designs can yield worst-case performances similar to those for worst-case designs when there is uncertainty in the network model. Improvements in network models for WDSs have the potential to yield significant improvements in CWS designs as well as more realistic evaluations of those designs. Although such improvements would be expected to yield improved CWS performance, the expected improvements in CWS performance have not been quantified previously. The results presented here should be useful to those responsible for the design or implementation of CWSs, particularly managers and engineers in water utilities, and encourage the development of improved network models.

Evaluation of Supply Chain Efficiency Based on a Novel Network of Data Envelopment Analysis Model

NASA Astrophysics Data System (ADS)

Fu, Li Fang; Meng, Jun; Liu, Ying

2015-12-01

Performance evaluation of supply chain (SC) is a vital topic in SC management and inherently complex problems with multilayered internal linkages and activities of multiple entities. Recently, various Network Data Envelopment Analysis (NDEA) models, which opened the “black box” of conventional DEA, were developed and applied to evaluate the complex SC with a multilayer network structure. However, most of them are input or output oriented models which cannot take into consideration the nonproportional changes of inputs and outputs simultaneously. This paper extends the Slack-based measure (SBM) model to a nonradial, nonoriented network model named as U-NSBM with the presence of undesirable outputs in the SC. A numerical example is presented to demonstrate the applicability of the model in quantifying the efficiency and ranking the supply chain performance. By comparing with the CCR and U-SBM models, it is shown that the proposed model has higher distinguishing ability and gives feasible solution in the presence of undesirable outputs. Meanwhile, it provides more insights for decision makers about the source of inefficiency as well as the guidance to improve the SC performance.

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

NASA Astrophysics Data System (ADS)

Puchaty, Ethan M.

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

Optimizing End-to-End Big Data Transfers over Terabits Network Infrastructure

DOE PAGES

Kim, Youngjae; Atchley, Scott; Vallee, Geoffroy R.; ...

2016-04-05

While future terabit networks hold the promise of significantly improving big-data motion among geographically distributed data centers, significant challenges must be overcome even on today's 100 gigabit networks to realize end-to-end performance. Multiple bottlenecks exist along the end-to-end path from source to sink, for instance, the data storage infrastructure at both the source and sink and its interplay with the wide-area network are increasingly the bottleneck to achieving high performance. In this study, we identify the issues that lead to congestion on the path of an end-to-end data transfer in the terabit network environment, and we present a new bulkmore » data movement framework for terabit networks, called LADS. LADS exploits the underlying storage layout at each endpoint to maximize throughput without negatively impacting the performance of shared storage resources for other users. LADS also uses the Common Communication Interface (CCI) in lieu of the sockets interface to benefit from hardware-level zero-copy, and operating system bypass capabilities when available. It can further improve data transfer performance under congestion on the end systems using buffering at the source using flash storage. With our evaluations, we show that LADS can avoid congested storage elements within the shared storage resource, improving input/output bandwidth, and data transfer rates across the high speed networks. We also investigate the performance degradation problems of LADS due to I/O contention on the parallel file system (PFS), when multiple LADS tools share the PFS. We design and evaluate a meta-scheduler to coordinate multiple I/O streams while sharing the PFS, to minimize the I/O contention on the PFS. Finally, with our evaluations, we observe that LADS with meta-scheduling can further improve the performance by up to 14 percent relative to LADS without meta-scheduling.« less

Optimizing End-to-End Big Data Transfers over Terabits Network Infrastructure

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

Kim, Youngjae; Atchley, Scott; Vallee, Geoffroy R.

While future terabit networks hold the promise of significantly improving big-data motion among geographically distributed data centers, significant challenges must be overcome even on today's 100 gigabit networks to realize end-to-end performance. Multiple bottlenecks exist along the end-to-end path from source to sink, for instance, the data storage infrastructure at both the source and sink and its interplay with the wide-area network are increasingly the bottleneck to achieving high performance. In this study, we identify the issues that lead to congestion on the path of an end-to-end data transfer in the terabit network environment, and we present a new bulkmore » data movement framework for terabit networks, called LADS. LADS exploits the underlying storage layout at each endpoint to maximize throughput without negatively impacting the performance of shared storage resources for other users. LADS also uses the Common Communication Interface (CCI) in lieu of the sockets interface to benefit from hardware-level zero-copy, and operating system bypass capabilities when available. It can further improve data transfer performance under congestion on the end systems using buffering at the source using flash storage. With our evaluations, we show that LADS can avoid congested storage elements within the shared storage resource, improving input/output bandwidth, and data transfer rates across the high speed networks. We also investigate the performance degradation problems of LADS due to I/O contention on the parallel file system (PFS), when multiple LADS tools share the PFS. We design and evaluate a meta-scheduler to coordinate multiple I/O streams while sharing the PFS, to minimize the I/O contention on the PFS. Finally, with our evaluations, we observe that LADS with meta-scheduling can further improve the performance by up to 14 percent relative to LADS without meta-scheduling.« less

Performance Evaluation and Control of Distributed Computer Communication Networks.

DTIC Science & Technology

1985-09-01

Zukerman, S. Katz, P. Rodriguez, R. Pazos , S. Resheff, Z. Tsai, Z. Zhang, L. Jong, V. Minh. Other participants are the following visiting... Pazos -Rangel "Bandwidth Allocation and Routing in ISDN’s," IEEE Communications Magazine, February 1984. Abstract The goal of communications network design...location and routing for integrated networks - is formulated, and efficient methods for its solution are presented. (2) R.A. Pazos -Rangel "Evaluation

20 CFR 411.330 - How will SSA evaluate an EN's performance?

Code of Federal Regulations, 2014 CFR

2014-04-01

... 20 Employees' Benefits 2 2014-04-01 2014-04-01 false How will SSA evaluate an EN's performance? 411.330 Section 411.330 Employees' Benefits SOCIAL SECURITY ADMINISTRATION THE TICKET TO WORK AND SELF-SUFFICIENCY PROGRAM Employment Networks § 411.330 How will SSA evaluate an EN's performance? (a) We will...

20 CFR 411.330 - How will SSA evaluate an EN's performance?

Code of Federal Regulations, 2013 CFR

2013-04-01

... 20 Employees' Benefits 2 2013-04-01 2013-04-01 false How will SSA evaluate an EN's performance? 411.330 Section 411.330 Employees' Benefits SOCIAL SECURITY ADMINISTRATION THE TICKET TO WORK AND SELF-SUFFICIENCY PROGRAM Employment Networks § 411.330 How will SSA evaluate an EN's performance? (a) We will...

20 CFR 411.330 - How will SSA evaluate an EN's performance?

Code of Federal Regulations, 2012 CFR

2012-04-01

... 20 Employees' Benefits 2 2012-04-01 2012-04-01 false How will SSA evaluate an EN's performance? 411.330 Section 411.330 Employees' Benefits SOCIAL SECURITY ADMINISTRATION THE TICKET TO WORK AND SELF-SUFFICIENCY PROGRAM Employment Networks § 411.330 How will SSA evaluate an EN's performance? (a) We will...

20 CFR 411.330 - How will SSA evaluate an EN's performance?

Code of Federal Regulations, 2010 CFR

2010-04-01

... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false How will SSA evaluate an EN's performance? 411.330 Section 411.330 Employees' Benefits SOCIAL SECURITY ADMINISTRATION THE TICKET TO WORK AND SELF-SUFFICIENCY PROGRAM Employment Networks § 411.330 How will SSA evaluate an EN's performance? (a) We will...

20 CFR 411.330 - How will SSA evaluate an EN's performance?

Code of Federal Regulations, 2011 CFR

2011-04-01

... 20 Employees' Benefits 2 2011-04-01 2011-04-01 false How will SSA evaluate an EN's performance? 411.330 Section 411.330 Employees' Benefits SOCIAL SECURITY ADMINISTRATION THE TICKET TO WORK AND SELF-SUFFICIENCY PROGRAM Employment Networks § 411.330 How will SSA evaluate an EN's performance? (a) We will...

Simplified Techniques for Evaluation and Interpretation of Pavement Deflections for Network-level Analysis : Guide for Assessment of Pavement Structure Performance for PMS Applications

DOT National Transportation Integrated Search

2012-06-01

The objective of this study was to develop an approach for incorporating techniques to interpret and evaluate deflection : data for network-level pavement management system (PMS) applications. The first part of this research focused on : identifying ...

Criteria for Evaluating Alternative Network and Link Layer Protocols for the NASA Constellation Program Communication Architecture

NASA Technical Reports Server (NTRS)

Benbenek, Daniel; Soloff, Jason; Lieb, Erica

2010-01-01

Selecting a communications and network architecture for future manned space flight requires an evaluation of the varying goals and objectives of the program, development of communications and network architecture evaluation criteria, and assessment of critical architecture trades. This paper uses Cx Program proposed exploration activities as a guideline; lunar sortie, outpost, Mars, and flexible path options are described. A set of proposed communications network architecture criteria are proposed and described. They include: interoperability, security, reliability, and ease of automating topology changes. Finally a key set of architecture options are traded including (1) multiplexing data at a common network layer vs. at the data link layer, (2) implementing multiple network layers vs. a single network layer, and (3) the use of a particular network layer protocol, primarily IPv6 vs. Delay Tolerant Networking (DTN). In summary, the protocol options are evaluated against the proposed exploration activities and their relative performance with respect to the criteria are assessed. An architectural approach which includes (a) the capability of multiplexing at both the network layer and the data link layer and (b) a single network layer for operations at each program phase, as these solutions are best suited to respond to the widest array of program needs and meet each of the evaluation criteria.

Using Fuzzy Logic for Performance Evaluation in Reinforcement Learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap S.

1992-01-01

Current reinforcement learning algorithms require long training periods which generally limit their applicability to small size problems. A new architecture is described which uses fuzzy rules to initialize its two neural networks: a neural network for performance evaluation and another for action selection. This architecture is applied to control of dynamic systems and it is demonstrated that it is possible to start with an approximate prior knowledge and learn to refine it through experiments using reinforcement learning.

Performance Evaluation of Multihop Packet Radio Networks by Simulation

DTIC Science & Technology

1987-03-01

Multihop Packet Radio Networks," Proc. IEEE, Vol. 75, No. 1, January 1987. [15] 1. Gitman , "On the Capacity of Slotted ALOHA Networks and Some Design...Networks in the Presence of Noise," Proc. Infocom, Washington D. C., April 1985 [40] H. Frank, I. Gitman and R. Van Slyke, " Packet Radio System

Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange

NASA Astrophysics Data System (ADS)

Jahangoshai Rezaee, Mustafa; Jozmaleki, Mehrdad; Valipour, Mahsa

2018-01-01

One of the main features to invest in stock exchange companies is their financial performance. On the other hand, conventional evaluation methods such as data envelopment analysis are not only a retrospective process, but are also a process, which are incomplete and ineffective approaches to evaluate the companies in the future. To remove this problem, it is required to plan an expert system for evaluating organizations when the online data are received from stock exchange market. This paper deals with an approach for predicting the online financial performance of companies when data are received in different time's intervals. The proposed approach is based on integrating fuzzy C-means (FCM), data envelopment analysis (DEA) and artificial neural network (ANN). The classical FCM method is unable to update the number of clusters and their members when the data are changed or the new data are received. Hence, this method is developed in order to make dynamic features for the number of clusters and clusters members in classical FCM. Then, DEA is used to evaluate DMUs by using financial ratios to provide targets in neural network. Finally, the designed network is trained and prepared for predicting companies' future performance. The data on Tehran Stock Market companies for six consecutive years (2007-2012) are used to show the abilities of the proposed approach.

Performance of an Abbreviated Version of the Lubben Social Network Scale among Three European Community-Dwelling Older Adult Populations

ERIC Educational Resources Information Center

Lubben, James; Blozik, Eva; Gillmann, Gerhard; Iliffe, Steve; von Renteln-Kruse, Wolfgang; Beck, John C.; Stuck, Andreas E.

2006-01-01

Purpose: There is a need for valid and reliable short scales that can be used to assess social networks and social supports and to screen for social isolation in older persons. Design and Methods: The present study is a cross-national and cross-cultural evaluation of the performance of an abbreviated version of the Lubben Social Network Scale…

A PERFORMANCE EVALUATION OF THE 2004 RELEASE OF MODELS-3 CMAQ

EPA Science Inventory

This performance evaluation compares a full annual simulation (2001) of CMAQ (Version4.4) covering the contiguous United States against monitoring data from four nationwide networks. This effort, which represents one of the most spatially and temporally comprehensive performance...

Performance Modeling of Network-Attached Storage Device Based Hierarchical Mass Storage Systems

NASA Technical Reports Server (NTRS)

Menasce, Daniel A.; Pentakalos, Odysseas I.

1995-01-01

Network attached storage devices improve I/O performance by separating control and data paths and eliminating host intervention during the data transfer phase. Devices are attached to both a high speed network for data transfer and to a slower network for control messages. Hierarchical mass storage systems use disks to cache the most recently used files and a combination of robotic and manually mounted tapes to store the bulk of the files in the file system. This paper shows how queuing network models can be used to assess the performance of hierarchical mass storage systems that use network attached storage devices as opposed to host attached storage devices. Simulation was used to validate the model. The analytic model presented here can be used, among other things, to evaluate the protocols involved in 1/0 over network attached devices.

Experimental demonstration of OpenFlow-enabled media ecosystem architecture for high-end applications over metro and core networks.

PubMed

Ntofon, Okung-Dike; Channegowda, Mayur P; Efstathiou, Nikolaos; Rashidi Fard, Mehdi; Nejabati, Reza; Hunter, David K; Simeonidou, Dimitra

2013-02-25

In this paper, a novel Software-Defined Networking (SDN) architecture is proposed for high-end Ultra High Definition (UHD) media applications. UHD media applications require huge amounts of bandwidth that can only be met with high-capacity optical networks. In addition, there are requirements for control frameworks capable of delivering effective application performance with efficient network utilization. A novel SDN-based Controller that tightly integrates application-awareness with network control and management is proposed for such applications. An OpenFlow-enabled test-bed demonstrator is reported with performance evaluations of advanced online and offline media- and network-aware schedulers.

Spatially distributed effects of mental exhaustion on resting-state FMRI networks.

PubMed

Esposito, Fabrizio; Otto, Tobias; Zijlstra, Fred R H; Goebel, Rainer

2014-01-01

Brain activity during rest is spatially coherent over functional connectivity networks called resting-state networks. In resting-state functional magnetic resonance imaging, independent component analysis yields spatially distributed network representations reflecting distinct mental processes, such as intrinsic (default) or extrinsic (executive) attention, and sensory inhibition or excitation. These aspects can be related to different treatments or subjective experiences. Among these, exhaustion is a common psychological state induced by prolonged mental performance. Using repeated functional magnetic resonance imaging sessions and spatial independent component analysis, we explored the effect of several hours of sustained cognitive performances on the resting human brain. Resting-state functional magnetic resonance imaging was performed on the same healthy volunteers in two days, with and without, and before, during and after, an intensive psychological treatment (skill training and sustained practice with a flight simulator). After each scan, subjects rated their level of exhaustion and performed an N-back task to evaluate eventual decrease in cognitive performance. Spatial maps of selected resting-state network components were statistically evaluated across time points to detect possible changes induced by the sustained mental performance. The intensive treatment had a significant effect on exhaustion and effort ratings, but no effects on N-back performances. Significant changes in the most exhausted state were observed in the early visual processing and the anterior default mode networks (enhancement) and in the fronto-parietal executive networks (suppression), suggesting that mental exhaustion is associated with a more idling brain state and that internal attention processes are facilitated to the detriment of more extrinsic processes. The described application may inspire future indicators of the level of fatigue in the neural attention system.

Metabolic networks are almost nonfractal: a comprehensive evaluation.

PubMed

Takemoto, Kazuhiro

2014-08-01

Network self-similarity or fractality are widely accepted as an important topological property of metabolic networks; however, recent studies cast doubt on the reality of self-similarity in the networks. Therefore, we perform a comprehensive evaluation of metabolic network fractality using a box-covering method with an earlier version and the latest version of metabolic networks and demonstrate that the latest metabolic networks are almost self-dissimilar, while the earlier ones are fractal, as reported in a number of previous studies. This result may be because the networks were randomized because of an increase in network density due to database updates, suggesting that the previously observed network fractality was due to a lack of available data on metabolic reactions. This finding may not entirely discount the importance of self-similarity of metabolic networks. Rather, it highlights the need for a more suitable definition of network fractality and a more careful examination of self-similarity of metabolic networks.

Enhancing End-to-End Performance of Information Services Over Ka-Band Global Satellite Networks

NASA Technical Reports Server (NTRS)

Bhasin, Kul B.; Glover, Daniel R.; Ivancic, William D.; vonDeak, Thomas C.

1997-01-01

The Internet has been growing at a rapid rate as the key medium to provide information services such as e-mail, WWW and multimedia etc., however its global reach is limited. Ka-band communication satellite networks are being developed to increase the accessibility of information services via the Internet at global scale. There is need to assess satellite networks in their ability to provide these services and interconnect seamlessly with existing and proposed terrestrial telecommunication networks. In this paper the significant issues and requirements in providing end-to-end high performance for the delivery of information services over satellite networks based on various layers in the OSI reference model are identified. Key experiments have been performed to evaluate the performance of digital video and Internet over satellite-like testbeds. The results of the early developments in ATM and TCP protocols over satellite networks are summarized.

PrismTech Data Distribution Service Java API Evaluation

NASA Technical Reports Server (NTRS)

Riggs, Cortney

2008-01-01

My internship duties with Launch Control Systems required me to start performance testing of an Object Management Group's (OMG) Data Distribution Service (DDS) specification implementation by PrismTech Limited through the Java programming language application programming interface (API). DDS is a networking middleware for Real-Time Data Distribution. The performance testing involves latency, redundant publishers, extended duration, redundant failover, and read performance. Time constraints allowed only for a data throughput test. I have designed the testing applications to perform all performance tests when time is allowed. Performance evaluation data such as megabits per second and central processing unit (CPU) time consumption were not easily attainable through the Java programming language; they required new methods and classes created in the test applications. Evaluation of this product showed the rate that data can be sent across the network. Performance rates are better on Linux platforms than AIX and Sun platforms. Compared to previous C++ programming language API, the performance evaluation also shows the language differences for the implementation. The Java API of the DDS has a lower throughput performance than the C++ API.

A framework for evaluating complex networks measurements

NASA Astrophysics Data System (ADS)

Comin, Cesar H.; Silva, Filipi N.; Costa, Luciano da F.

2015-06-01

A good deal of current research in complex networks involves the characterization and/or classification of the topological properties of given structures, which has motivated several respective measurements. This letter proposes a framework for evaluating the quality of complex-network measurements in terms of their effective resolution, degree of degeneracy and discriminability. The potential of the suggested approach is illustrated with respect to comparing the characterization of several model and real-world networks by using concentric and symmetry measurements. The results indicate a markedly superior performance for the latter type of mapping.

Delay and cost performance analysis of the diffie-hellman key exchange protocol in opportunistic mobile networks

NASA Astrophysics Data System (ADS)

Soelistijanto, B.; Muliadi, V.

2018-03-01

Diffie-Hellman (DH) provides an efficient key exchange system by reducing the number of cryptographic keys distributed in the network. In this method, a node broadcasts a single public key to all nodes in the network, and in turn each peer uses this key to establish a shared secret key which then can be utilized to encrypt and decrypt traffic between the peer and the given node. In this paper, we evaluate the key transfer delay and cost performance of DH in opportunistic mobile networks, a specific scenario of MANETs where complete end-to-end paths rarely exist between sources and destinations; consequently, the end-to-end delays in these networks are much greater than typical MANETs. Simulation results, driven by a random node movement model and real human mobility traces, showed that DH outperforms a typical key distribution scheme based on the RSA algorithm in terms of key transfer delay, measured by average key convergence time; however, DH performs as well as the benchmark in terms of key transfer cost, evaluated by total key (copies) forwards.

Performance Evaluation of Supercomputers using HPCC and IMB Benchmarks

NASA Technical Reports Server (NTRS)

Saini, Subhash; Ciotti, Robert; Gunney, Brian T. N.; Spelce, Thomas E.; Koniges, Alice; Dossa, Don; Adamidis, Panagiotis; Rabenseifner, Rolf; Tiyyagura, Sunil R.; Mueller, Matthias;

Evaluation of AL-FEC performance for IP television services QoS

NASA Astrophysics Data System (ADS)

Mammi, E.; Russo, G.; Neri, A.

2010-01-01

The IP television services quality is a critical issue because of the nature of transport infrastructure. Packet loss is the main cause of service degradation in such kind of network platforms. The use of forward error correction (FEC) techniques in the application layer (AL-FEC), between the source of TV service (video server) and the user terminal, seams to be an efficient strategy to counteract packet losses alternatively or in addiction to suitable traffic management policies (only feasible in "managed networks"). A number of AL-FEC techniques have been discussed in literature and proposed for inclusion in TV over IP international standards. In this paper a performance evaluation of the AL-FEC defined in SMPTE 2022-1 standard is presented. Different typical events occurring in IP networks causing different types (in terms of statistic distribution) of IP packet losses have been studied and AL-FEC performance to counteract these kind of losses have been evaluated. The performed analysis has been carried out in view of fulfilling the TV services QoS requirements that are usually very demanding. For managed networks, this paper envisages a strategy to combine the use of AL-FEC with the set-up of a transport quality based on FEC packets prioritization. Promising results regard this kind of strategy have been obtained.

Testing the Feasibility of a Low-Cost Network Performance Measurement Infrastructure

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

Chevalier, Scott; Schopf, Jennifer M.; Miller, Kenneth

2016-07-01

Todays science collaborations depend on reliable, high performance networks, but monitoring the end-to-end performance of a network can be costly and difficult. The most accurate approaches involve using measurement equipment in many locations, which can be both expensive and difficult to manage due to immobile or complicated assets. The perfSONAR framework facilitates network measurement making management of the tests more reasonable. Traditional deployments have used over-provisioned servers, which can be expensive to deploy and maintain. As scientific network uses proliferate, there is a desire to instrument more facets of a network to better understand trends. This work explores low costmore » alternatives to assist with network measurement. Benefits include the ability to deploy more resources quickly, and reduced capital and operating expenditures. Finally, we present candidate platforms and a testing scenario that evaluated the relative merits of four types of small form factor equipment to deliver accurate performance measurements.« less

Ad Hoc Network Architecture for Multi-Media Networks

DTIC Science & Technology

2007-12-01

sensor network . Video traffic is modeled and simulations are performed via the use of the Sun Small Programmable Object Technology (Sun SPOT) Java...characteristics of video traffic must be studied and understood. This thesis focuses on evaluating the possibility of routing video images over a wireless

Balanced excitation and inhibition are required for high-capacity, noise-robust neuronal selectivity

PubMed Central

Abbott, L. F.; Sompolinsky, Haim

2017-01-01

Neurons and networks in the cerebral cortex must operate reliably despite multiple sources of noise. To evaluate the impact of both input and output noise, we determine the robustness of single-neuron stimulus selective responses, as well as the robustness of attractor states of networks of neurons performing memory tasks. We find that robustness to output noise requires synaptic connections to be in a balanced regime in which excitation and inhibition are strong and largely cancel each other. We evaluate the conditions required for this regime to exist and determine the properties of networks operating within it. A plausible synaptic plasticity rule for learning that balances weight configurations is presented. Our theory predicts an optimal ratio of the number of excitatory and inhibitory synapses for maximizing the encoding capacity of balanced networks for given statistics of afferent activations. Previous work has shown that balanced networks amplify spatiotemporal variability and account for observed asynchronous irregular states. Here we present a distinct type of balanced network that amplifies small changes in the impinging signals and emerges automatically from learning to perform neuronal and network functions robustly. PMID:29042519

GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods.

PubMed

Schaffter, Thomas; Marbach, Daniel; Floreano, Dario

2011-08-15

Over the last decade, numerous methods have been developed for inference of regulatory networks from gene expression data. However, accurate and systematic evaluation of these methods is hampered by the difficulty of constructing adequate benchmarks and the lack of tools for a differentiated analysis of network predictions on such benchmarks. Here, we describe a novel and comprehensive method for in silico benchmark generation and performance profiling of network inference methods available to the community as an open-source software called GeneNetWeaver (GNW). In addition to the generation of detailed dynamical models of gene regulatory networks to be used as benchmarks, GNW provides a network motif analysis that reveals systematic prediction errors, thereby indicating potential ways of improving inference methods. The accuracy of network inference methods is evaluated using standard metrics such as precision-recall and receiver operating characteristic curves. We show how GNW can be used to assess the performance and identify the strengths and weaknesses of six inference methods. Furthermore, we used GNW to provide the international Dialogue for Reverse Engineering Assessments and Methods (DREAM) competition with three network inference challenges (DREAM3, DREAM4 and DREAM5). GNW is available at http://gnw.sourceforge.net along with its Java source code, user manual and supporting data. Supplementary data are available at Bioinformatics online. dario.floreano@epfl.ch.

Performance of an artificial neural network for vertical root fracture detection: an ex vivo study.

PubMed

Kositbowornchai, Suwadee; Plermkamon, Supattra; Tangkosol, Tawan

2013-04-01

To develop an artificial neural network for vertical root fracture detection. A probabilistic neural network design was used to clarify whether a tooth root was sound or had a vertical root fracture. Two hundred images (50 sound and 150 vertical root fractures) derived from digital radiography--used to train and test the artificial neural network--were divided into three groups according to the number of training and test data sets: 80/120,105/95 and 130/70, respectively. Either training or tested data were evaluated using grey-scale data per line passing through the root. These data were normalized to reduce the grey-scale variance and fed as input data of the neural network. The variance of function in recognition data was calculated between 0 and 1 to select the best performance of neural network. The performance of the neural network was evaluated using a diagnostic test. After testing data under several variances of function, we found the highest sensitivity (98%), specificity (90.5%) and accuracy (95.7%) occurred in Group three, for which the variance of function in recognition data was between 0.025 and 0.005. The neural network designed in this study has sufficient sensitivity, specificity and accuracy to be a model for vertical root fracture detection. © 2012 John Wiley & Sons A/S.

Performance Evaluation Model for Application Layer Firewalls.

PubMed

Xuan, Shichang; Yang, Wu; Dong, Hui; Zhang, Jiangchuan

2016-01-01

Application layer firewalls protect the trusted area network against information security risks. However, firewall performance may affect user experience. Therefore, performance analysis plays a significant role in the evaluation of application layer firewalls. This paper presents an analytic model of the application layer firewall, based on a system analysis to evaluate the capability of the firewall. In order to enable users to improve the performance of the application layer firewall with limited resources, resource allocation was evaluated to obtain the optimal resource allocation scheme in terms of throughput, delay, and packet loss rate. The proposed model employs the Erlangian queuing model to analyze the performance parameters of the system with regard to the three layers (network, transport, and application layers). Then, the analysis results of all the layers are combined to obtain the overall system performance indicators. A discrete event simulation method was used to evaluate the proposed model. Finally, limited service desk resources were allocated to obtain the values of the performance indicators under different resource allocation scenarios in order to determine the optimal allocation scheme. Under limited resource allocation, this scheme enables users to maximize the performance of the application layer firewall.

An Expedient Study on Back-Propagation (BPN) Neural Networks for Modeling Automated Evaluation of the Answers and Progress of Deaf Students' That Possess Basic Knowledge of the English Language and Computer Skills

NASA Astrophysics Data System (ADS)

Vrettaros, John; Vouros, George; Drigas, Athanasios S.

This article studies the expediency of using neural networks technology and the development of back-propagation networks (BPN) models for modeling automated evaluation of the answers and progress of deaf students' that possess basic knowledge of the English language and computer skills, within a virtual e-learning environment. The performance of the developed neural models is evaluated with the correlation factor between the neural networks' response values and the real value data as well as the percentage measurement of the error between the neural networks' estimate values and the real value data during its training process and afterwards with unknown data that weren't used in the training process.

Understanding the Performance and Potential of Cloud Computing for Scientific Applications

DOE PAGES

Sadooghi, Iman; Martin, Jesus Hernandez; Li, Tonglin; ...

2015-02-19

In this paper, commercial clouds bring a great opportunity to the scientific computing area. Scientific applications usually require significant resources, however not all scientists have access to sufficient high-end computing systems, may of which can be found in the Top500 list. Cloud Computing has gained the attention of scientists as a competitive resource to run HPC applications at a potentially lower cost. But as a different infrastructure, it is unclear whether clouds are capable of running scientific applications with a reasonable performance per money spent. This work studies the performance of public clouds and places this performance in context tomore » price. We evaluate the raw performance of different services of AWS cloud in terms of the basic resources, such as compute, memory, network and I/O. We also evaluate the performance of the scientific applications running in the cloud. This paper aims to assess the ability of the cloud to perform well, as well as to evaluate the cost of the cloud running scientific applications. We developed a full set of metrics and conducted a comprehensive performance evlauation over the Amazon cloud. We evaluated EC2, S3, EBS and DynamoDB among the many Amazon AWS services. We evaluated the memory sub-system performance with CacheBench, the network performance with iperf, processor and network performance with the HPL benchmark application, and shared storage with NFS and PVFS in addition to S3. We also evaluated a real scientific computing application through the Swift parallel scripting system at scale. Armed with both detailed benchmarks to gauge expected performance and a detailed monetary cost analysis, we expect this paper will be a recipe cookbook for scientists to help them decide where to deploy and run their scientific applications between public clouds, private clouds, or hybrid clouds.« less

Understanding the Performance and Potential of Cloud Computing for Scientific Applications

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

Sadooghi, Iman; Martin, Jesus Hernandez; Li, Tonglin

In this paper, commercial clouds bring a great opportunity to the scientific computing area. Scientific applications usually require significant resources, however not all scientists have access to sufficient high-end computing systems, may of which can be found in the Top500 list. Cloud Computing has gained the attention of scientists as a competitive resource to run HPC applications at a potentially lower cost. But as a different infrastructure, it is unclear whether clouds are capable of running scientific applications with a reasonable performance per money spent. This work studies the performance of public clouds and places this performance in context tomore » price. We evaluate the raw performance of different services of AWS cloud in terms of the basic resources, such as compute, memory, network and I/O. We also evaluate the performance of the scientific applications running in the cloud. This paper aims to assess the ability of the cloud to perform well, as well as to evaluate the cost of the cloud running scientific applications. We developed a full set of metrics and conducted a comprehensive performance evlauation over the Amazon cloud. We evaluated EC2, S3, EBS and DynamoDB among the many Amazon AWS services. We evaluated the memory sub-system performance with CacheBench, the network performance with iperf, processor and network performance with the HPL benchmark application, and shared storage with NFS and PVFS in addition to S3. We also evaluated a real scientific computing application through the Swift parallel scripting system at scale. Armed with both detailed benchmarks to gauge expected performance and a detailed monetary cost analysis, we expect this paper will be a recipe cookbook for scientists to help them decide where to deploy and run their scientific applications between public clouds, private clouds, or hybrid clouds.« less

Optical burst switching for the next generation Optical Internet

NASA Astrophysics Data System (ADS)

Yoo, Myungsik

2000-11-01

In recent years, Internet Protocol (IP) over Wavelength Division Multiplexing (WDM) networks for the next generation Internet (or the so-called Optical Internet) have received enormous attention. There are two main drivers for an Optical Internet. One is the explosion of Internet traffic, which seems to keep growing exponentially. The other driver is the rapid advance in the WDM optical networking technology. In this study, key issues in the optical (WDM) layer will be investigated. As a novel switching paradigm for Optical Internet, Optical Burst Switching (OBS) is discussed. By leveraging the attractive properties of optical communications and at the same time, taking into account its limitations, OBS can combine the best of optical circuit-switching and packet/cell switching. The general concept of JET-based OBS protocol is described, including offset time and delayed reservation. In the next generation Optical Internet, one must address how to support Quality of Service (QoS) at the WDM layer since current IP provides only best effort services. The offset-time- based QoS scheme is proposed as a way of supporting QoS at the WDM layer. Unlike existing QoS schemes, offset- time-based QoS scheme does not mandate the use of buffer to differentiate services. For the bufferless WDM switch, the performance of offset- time-based QoS scheme is evaluated in term of blocking probability. In addition, the extra offset time required for class isolation is quantified and the theoretical bounds on blocking probability are analyzed. The offset-time-based scheme is applied to WDM switch with limited fiber delay line (FDL) buffer. We evaluate the effect of having a FDL buffer on the QoS performance of the offset-time-based scheme in terms of the loss probability and queuing delay of bursts. Finally, in order to dimension the network resources in Optical Internet backbone networks, the performance of the offset-time-based QoS scheme is evaluated for the multi-hop case. In particular, we consider very high performance Backbone Network Service (vBNS) backbone network. Various policies such as drop, retransmission, deflection routing and buffering are considered for performance evaluation. The performance results obtained under these policies are compared to decide the most efficient policy for the WDM backbone network.

Safety performance functions for intersections : final report, December 2009.

DOT National Transportation Integrated Search

2009-12-01

Road safety management activities include screening the network for sites with a potential for safety improvement (Network : Screening), diagnosing safety problems at specific sites, and evaluating the safety effectiveness of implemented : countermea...

Learning, memory, and the role of neural network architecture.

PubMed

Hermundstad, Ann M; Brown, Kevin S; Bassett, Danielle S; Carlson, Jean M

2011-06-01

The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems.

Assessing the Robustness of Graph Statistics for Network Analysis Under Incomplete Information

DTIC Science & Technology

strategy for dismantling these networks based on their network structure. However, these strategies typically assume complete information about the...combat them with missing information . This thesis analyzes the performance of a variety of network statistics in the context of incomplete information by...leveraging simulation to remove nodes and edges from networks and evaluating the effect this missing information has on our ability to accurately

Analysis of 2D Torus and Hub Topologies of 100Mb/s Ethernet for the Whitney Commodity Computing Testbed

NASA Technical Reports Server (NTRS)

Pedretti, Kevin T.; Fineberg, Samuel A.; Kutler, Paul (Technical Monitor)

1997-01-01

A variety of different network technologies and topologies are currently being evaluated as part of the Whitney Project. This paper reports on the implementation and performance of a Fast Ethernet network configured in a 4x4 2D torus topology in a testbed cluster of 'commodity' Pentium Pro PCs. Several benchmarks were used for performance evaluation: an MPI point to point message passing benchmark, an MPI collective communication benchmark, and the NAS Parallel Benchmarks version 2.2 (NPB2). Our results show that for point to point communication on an unloaded network, the hub and 1 hop routes on the torus have about the same bandwidth and latency. However, the bandwidth decreases and the latency increases on the torus for each additional route hop. Collective communication benchmarks show that the torus provides roughly four times more aggregate bandwidth and eight times faster MPI barrier synchronizations than a hub based network for 16 processor systems. Finally, the SOAPBOX benchmarks, which simulate real-world CFD applications, generally demonstrated substantially better performance on the torus than on the hub. In the few cases the hub was faster, the difference was negligible. In total, our experimental results lead to the conclusion that for Fast Ethernet networks, the torus topology has better performance and scales better than a hub based network.

External quality assessment for CD4 + T-lymphocyte count test: Performance of the Brazilian public health laboratories network.

PubMed

Gaspar, Pâmela Cristina; Wohlke, Bruna Lovizutto Protti; Brunialti, Milena Karina Coló; Pires, Ana Flávia; Kohiyama, Igor Massaki; Salomão, Reinaldo; Alonso Neto, José Boullosa; Júnior, Orlando da Costa Ferreira; Franchini, Miriam; Bazzo, Maria Luiza; Benzaken, Adele Schwartz

2018-05-01

The National Network for CD4+ T-lymphocyte counting of Brazil comprises 93 laboratories. This study reports the laboratory performances achieved in external quality assessment (EQA) rounds provides by Ministry of Health to evaluate the quality of the kits used and the performance of test by the technicians.Ten EQA rounds were analyzed according the EQA criteria aimed to evaluate individual laboratory performance on the basis of the accuracy of their results compared to the general mean obtained by all participating laboratories and the reproducibility of the results obtained between 2 samples from the same donor.The percentage of approved and failed laboratories in the EQAs tends to follow a uniform pattern. Since 2011, approval has remained above 80% and the failure rate has never exceeded 15%.EQA is very important to evaluate the performance of the laboratories, to identify monitor, and to resolve errors as quickly as possible.

Traffic Management for Satellite-ATM Networks

NASA Technical Reports Server (NTRS)

Goyal, Rohit; Jain, Raj; Fahmy, Sonia; Vandalore, Bobby; Goyal, Mukul

1998-01-01

Various issues associated with "Traffic Management for Satellite-ATM Networks" are presented in viewgraph form. Specific topics include: 1) Traffic management issues for TCP/IP based data services over satellite-ATM networks; 2) Design issues for TCP/IP over ATM; 3) Optimization of the performance of TCP/IP over ATM for long delay networks; and 4) Evaluation of ATM service categories for TCP/IP traffic.

FIELD EVALUATION OF SAMPLERS FOR EPA'S NATIONAL PM 2.5 CHEMICAL SPECIATION NETWORK-PRELIMINARY RESULTS FROM ATLANTA

EPA Science Inventory

The US EPA bas established a national network at nearly 1100 sites to monitor PM2.5 mass for testing compliance with the PM2.5 National Ambient Air Quality Standards. The objective of the field evaluation is to determine the performance characteristics for the collection of the...

A Wireless Sensor Network-Based Portable Vehicle Detector Evaluation System

PubMed Central

Yoo, Seong-eun

2013-01-01

In an upcoming smart transportation environment, performance evaluations of existing Vehicle Detection Systems are crucial to maintain their accuracy. The existing evaluation method for Vehicle Detection Systems is based on a wired Vehicle Detection System reference and a video recorder, which must be operated and analyzed by capable traffic experts. However, this conventional evaluation system has many disadvantages. It is inconvenient to deploy, the evaluation takes a long time, and it lacks scalability and objectivity. To improve the evaluation procedure, this paper proposes a Portable Vehicle Detector Evaluation System based on wireless sensor networks. We describe both the architecture and design of a Vehicle Detector Evaluation System and the implementation results, focusing on the wireless sensor networks and methods for traffic information measurement. With the help of wireless sensor networks and automated analysis, our Vehicle Detector Evaluation System can evaluate a Vehicle Detection System conveniently and objectively. The extensive evaluations of our Vehicle Detector Evaluation System show that it can measure the traffic information such as volume counts and speed with over 98% accuracy. PMID:23344388

A wireless sensor network-based portable vehicle detector evaluation system.

PubMed

Yoo, Seong-eun

2013-01-17

In an upcoming smart transportation environment, performance evaluations of existing Vehicle Detection Systems are crucial to maintain their accuracy. The existing evaluation method for Vehicle Detection Systems is based on a wired Vehicle Detection System reference and a video recorder, which must be operated and analyzed by capable traffic experts. However, this conventional evaluation system has many disadvantages. It is inconvenient to deploy, the evaluation takes a long time, and it lacks scalability and objectivity. To improve the evaluation procedure, this paper proposes a Portable Vehicle Detector Evaluation System based on wireless sensor networks. We describe both the architecture and design of a Vehicle Detector Evaluation System and the implementation results, focusing on the wireless sensor networks and methods for traffic information measurement. With the help of wireless sensor networks and automated analysis, our Vehicle Detector Evaluation System can evaluate a Vehicle Detection System conveniently and objectively. The extensive evaluations of our Vehicle Detector Evaluation System show that it can measure the traffic information such as volume counts and speed with over 98% accuracy.

Evaluation on surface current observing network of high frequency ground wave radars in the Gulf of Thailand

NASA Astrophysics Data System (ADS)

Yin, Xunqiang; Shi, Junqiang; Qiao, Fangli

2018-05-01

Due to the high cost of ocean observation system, the scientific design of observation network becomes much important. The current network of the high frequency radar system in the Gulf of Thailand has been studied using a three-dimensional coastal ocean model. At first, the observations from current radars have been assimilated into this coastal model and the forecast results have improved due to the data assimilation. But the results also show that further optimization of the observing network is necessary. And then, a series of experiments were carried out to assess the performance of the existing high frequency ground wave radar surface current observation system. The simulated surface current data in three regions were assimilated sequentially using an efficient ensemble Kalman filter data assimilation scheme. The experimental results showed that the coastal surface current observation system plays a positive role in improving the numerical simulation of the currents. Compared with the control experiment without assimilation, the simulation precision of surface and subsurface current had been improved after assimilated the surface currents observed at current networks. However, the improvement for three observing regions was quite different and current observing network in the Gulf of Thailand is not effective and a further optimization is required. Based on these evaluations, a manual scheme has been designed by discarding the redundant and inefficient locations and adding new stations where the performance after data assimilation is still low. For comparison, an objective scheme based on the idea of data assimilation has been obtained. Results show that all the two schemes of observing network perform better than the original network and optimal scheme-based data assimilation is much superior to the manual scheme that based on the evaluation of original observing network in the Gulf of Thailand. The distributions of the optimal network of radars could be a useful guidance for future design of observing system in this region.

Historical performance evaluation of Iowa pavement treatments using data analytics : final report.

DOT National Transportation Integrated Search

2016-11-01

The pavement network in Iowa has reached a mature state making maintenance and rehabilitation activities more important than new construction. As such, a need exists to evaluate the performance of the pavement treatments and estimate their performanc...

The Strength of the Strongest Ties in Collaborative Problem Solving

NASA Astrophysics Data System (ADS)

de Montjoye, Yves-Alexandre; Stopczynski, Arkadiusz; Shmueli, Erez; Pentland, Alex; Lehmann, Sune

2014-06-01

Complex problem solving in science, engineering, and business has become a highly collaborative endeavor. Teams of scientists or engineers collaborate on projects using their social networks to gather new ideas and feedback. Here we bridge the literature on team performance and information networks by studying teams' problem solving abilities as a function of both their within-team networks and their members' extended networks. We show that, while an assigned team's performance is strongly correlated with its networks of expressive and instrumental ties, only the strongest ties in both networks have an effect on performance. Both networks of strong ties explain more of the variance than other factors, such as measured or self-evaluated technical competencies, or the personalities of the team members. In fact, the inclusion of the network of strong ties renders these factors non-significant in the statistical analysis. Our results have consequences for the organization of teams of scientists, engineers, and other knowledge workers tackling today's most complex problems.

The strength of the strongest ties in collaborative problem solving.

PubMed

de Montjoye, Yves-Alexandre; Stopczynski, Arkadiusz; Shmueli, Erez; Pentland, Alex; Lehmann, Sune

2014-06-20

Complex problem solving in science, engineering, and business has become a highly collaborative endeavor. Teams of scientists or engineers collaborate on projects using their social networks to gather new ideas and feedback. Here we bridge the literature on team performance and information networks by studying teams' problem solving abilities as a function of both their within-team networks and their members' extended networks. We show that, while an assigned team's performance is strongly correlated with its networks of expressive and instrumental ties, only the strongest ties in both networks have an effect on performance. Both networks of strong ties explain more of the variance than other factors, such as measured or self-evaluated technical competencies, or the personalities of the team members. In fact, the inclusion of the network of strong ties renders these factors non-significant in the statistical analysis. Our results have consequences for the organization of teams of scientists, engineers, and other knowledge workers tackling today's most complex problems.

Performance analysis and improvement of WPAN MAC for home networks.

PubMed

Mehta, Saurabh; Kwak, Kyung Sup

2010-01-01

The wireless personal area network (WPAN) is an emerging wireless technology for future short range indoor and outdoor communication applications. The IEEE 802.15.3 medium access control (MAC) is proposed to coordinate the access to the wireless medium among the competing devices, especially for short range and high data rate applications in home networks. In this paper we use analytical modeling to study the performance analysis of WPAN (IEEE 802.15.3) MAC in terms of throughput, efficient bandwidth utilization, and delay with various ACK policies under error channel condition. This allows us to introduce a K-Dly-ACK-AGG policy, payload size adjustment mechanism, and Improved Backoff algorithm to improve the performance of the WPAN MAC. Performance evaluation results demonstrate the impact of our improvements on network capacity. Moreover, these results can be very useful to WPAN application designers and protocol architects to easily and correctly implement WPAN for home networking.

Performance of highly connected photonic switching lossless metro-access optical networks

NASA Astrophysics Data System (ADS)

Martins, Indayara Bertoldi; Martins, Yara; Barbosa, Felipe Rudge

2018-03-01

The present work analyzes the performance of photonic switching networks, optical packet switching (OPS) and optical burst switching (OBS), in mesh topology of different sizes and configurations. The "lossless" photonic switching node is based on a semiconductor optical amplifier, demonstrated and validated with experimental results on optical power gain, noise figure, and spectral range. The network performance was evaluated through computer simulations based on parameters such as average number of hops, optical packet loss fraction, and optical transport delay (Am). The combination of these elements leads to a consistent account of performance, in terms of network traffic and packet delivery for OPS and OBS metropolitan networks. Results show that a combination of highly connected mesh topologies having an ingress e-buffer present high efficiency and throughput, with very low packet loss and low latency, ensuring fast data delivery to the final receiver.

Performance Analysis and Improvement of WPAN MAC for Home Networks

PubMed Central

Mehta, Saurabh; Kwak, Kyung Sup

2010-01-01

The wireless personal area network (WPAN) is an emerging wireless technology for future short range indoor and outdoor communication applications. The IEEE 802.15.3 medium access control (MAC) is proposed to coordinate the access to the wireless medium among the competing devices, especially for short range and high data rate applications in home networks. In this paper we use analytical modeling to study the performance analysis of WPAN (IEEE 802.15.3) MAC in terms of throughput, efficient bandwidth utilization, and delay with various ACK policies under error channel condition. This allows us to introduce a K-Dly-ACK-AGG policy, payload size adjustment mechanism, and Improved Backoff algorithm to improve the performance of the WPAN MAC. Performance evaluation results demonstrate the impact of our improvements on network capacity. Moreover, these results can be very useful to WPAN application designers and protocol architects to easily and correctly implement WPAN for home networking. PMID:22319274

Proton Single Event Effects (SEE) Testing of the Myrinet Crossbar Switch and Network Interface Card

NASA Technical Reports Server (NTRS)

Howard, James W., Jr.; LaBel, Kenneth A.; Carts, Martin A.; Stattel, Ronald; Irwin, Timothy L.; Day, John H. (Technical Monitor)

2002-01-01

As part of the Remote Exploration and Experimentation Project (REE), work was performed to do a proton SEE (Single Event Effect) evaluation of the Myricom network protocol system (Myrinet). This testing included the evaluation of the Myrinet crossbar switch and the Network Interface Card (NIC). To this end, two crossbar switch devices and five components in the NIC were exposed to the proton beam at the University of California at Davis Crocker Nuclear Laboratory (CNL).

Strategies for a better performance of RPL under mobility in wireless sensor networks

NASA Astrophysics Data System (ADS)

Latib, Z. A.; Jamil, A.; Alduais, N. A. M.; Abdullah, J.; Audah, L. H. M.; Alias, R.

2017-09-01

A Wireless Sensor Network (WSN) is usually stationary, which the network comprises of static nodes. The increase demand for mobility in various applications such as environmental monitoring, medical, home automation, and military, raises the question how IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) would perform under these mobility applications. This paper aims to understand performance of RPL and come out with strategies for a better performance of RPL in mobility scenarios. Because of this, this paper evaluates the performance of the RPL protocol under three different scenarios: sink and sensor nodes are static, static sink and mobile sensor nodes, and sink and sensor nodes are mobile. The network scenarios are implemented in Cooja simulator. A WSN consists of 25 sensor nodes and one sink node is configured in the simulation environment. The simulation is varied over different packet rates and ContikiMAC's Clear Channel Assessment (CCA) rate. As the performance metric, RPL is evaluated in term of packet delivery ratio (PDR), power consumption and packet rates. The simulation results show RPL provides a poor PDR in the mobility scenarios when compared to the static scenario. In addition, RPL consumes more power and increases duty-cycle rate to support mobility when compared to the static scenario. Based on the findings, we suggest three strategies for a better performance of RPL in mobility scenarios. First, RPL should operates at a lower packet rates when implemented in the mobility scenarios. Second, RPL should be implemented with a higher duty-cycle rate. Lastly, the sink node should be positioned as much as possible in the center of the mobile network.

On the Network Convergence Process in RPL over IEEE 802.15.4 Multihop Networks: Improvement and Trade-Offs

PubMed Central

Kermajani, Hamidreza; Gomez, Carles

2014-01-01

The IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) has been recently developed by the Internet Engineering Task Force (IETF). Given its crucial role in enabling the Internet of Things, a significant amount of research effort has already been devoted to RPL. However, the RPL network convergence process has not yet been investigated in detail. In this paper we study the influence of the main RPL parameters and mechanisms on the network convergence process of this protocol in IEEE 802.15.4 multihop networks. We also propose and evaluate a mechanism that leverages an option available in RPL for accelerating the network convergence process. We carry out extensive simulations for a wide range of conditions, considering different network scenarios in terms of size and density. Results show that network convergence performance depends dramatically on the use and adequate configuration of key RPL parameters and mechanisms. The findings and contributions of this work provide a RPL configuration guideline for network convergence performance tuning, as well as a characterization of the related performance trade-offs. PMID:25004154

On the network convergence process in RPL over IEEE 802.15.4 multihop networks: improvement and trade-offs.

PubMed

Kermajani, Hamidreza; Gomez, Carles

2014-07-07

The IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) has been recently developed by the Internet Engineering Task Force (IETF). Given its crucial role in enabling the Internet of Things, a significant amount of research effort has already been devoted to RPL. However, the RPL network convergence process has not yet been investigated in detail. In this paper we study the influence of the main RPL parameters and mechanisms on the network convergence process of this protocol in IEEE 802.15.4 multihop networks. We also propose and evaluate a mechanism that leverages an option available in RPL for accelerating the network convergence process. We carry out extensive simulations for a wide range of conditions, considering different network scenarios in terms of size and density. Results show that network convergence performance depends dramatically on the use and adequate configuration of key RPL parameters and mechanisms. The findings and contributions of this work provide a RPL configuration guideline for network convergence performance tuning, as well as a characterization of the related performance trade-offs.

Optimizing the ASC WAN: evaluating network performance tools for comparing transport protocols.

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

Lydick, Christopher L.

2007-07-01

The Advanced Simulation & Computing Wide Area Network (ASC WAN), which is a high delay-bandwidth network connection between US Department of Energy National Laboratories, is constantly being examined and evaluated for efficiency. One of the current transport-layer protocols which is used, TCP, was developed for traffic demands which are different from that on the ASC WAN. The Stream Control Transport Protocol (SCTP), on the other hand, has shown characteristics which make it more appealing to networks such as these. Most important, before considering a replacement for TCP on any network, a testing tool that performs well against certain criteria needsmore » to be found. In order to try to find such a tool, two popular networking tools (Netperf v.2.4.3 & v.2.4.6 (OpenSS7 STREAMS), and Iperf v.2.0.6) were tested. These tools implement both TCP and SCTP and were evaluated using four metrics: (1) How effectively can the tool reach a throughput near the bandwidth? (2) How much of the CPU does the tool utilize during operation? (3) Is the tool freely and widely available? And, (4) Is the tool actively developed? Following the analysis of those tools, this paper goes further into explaining some recommendations and ideas for future work.« less

Evaluating the Fraser Health Balanced Scorecard--a formative evaluation.

PubMed

Barnardo, Catherine; Jivanni, Amin

2009-01-01

Fraser Health (FH), a large, Canadian, integrated health care network, adopted the Balanced Scorecard (BSC) approach to monitor organizational performance in 2006. This paper reports on the results of a formative evaluation, conducted in April, 2008, to assess the usefulness of the BSC as a performance-reporting system and a performance management tool. Results indicated that the BSC has proven to be useful for reporting performance but is not currently used for performance management in a substantial way.

Comparison of different artificial neural network architectures in modeling of Chlorella sp. flocculation.

PubMed

Zenooz, Alireza Moosavi; Ashtiani, Farzin Zokaee; Ranjbar, Reza; Nikbakht, Fatemeh; Bolouri, Oberon

2017-07-03

Biodiesel production from microalgae feedstock should be performed after growth and harvesting of the cells, and the most feasible method for harvesting and dewatering of microalgae is flocculation. Flocculation modeling can be used for evaluation and prediction of its performance under different affective parameters. However, the modeling of flocculation in microalgae is not simple and has not performed yet, under all experimental conditions, mostly due to different behaviors of microalgae cells during the process under different flocculation conditions. In the current study, the modeling of microalgae flocculation is studied with different neural network architectures. Microalgae species, Chlorella sp., was flocculated with ferric chloride under different conditions and then the experimental data modeled using artificial neural network. Neural network architectures of multilayer perceptron (MLP) and radial basis function architectures, failed to predict the targets successfully, though, modeling was effective with ensemble architecture of MLP networks. Comparison between the performances of the ensemble and each individual network explains the ability of the ensemble architecture in microalgae flocculation modeling.

An enhanced performance through agent-based secure approach for mobile ad hoc networks

NASA Astrophysics Data System (ADS)

Bisen, Dhananjay; Sharma, Sanjeev

2018-01-01

This paper proposes an agent-based secure enhanced performance approach (AB-SEP) for mobile ad hoc network. In this approach, agent nodes are selected through optimal node reliability as a factor. This factor is calculated on the basis of node performance features such as degree difference, normalised distance value, energy level, mobility and optimal hello interval of node. After selection of agent nodes, a procedure of malicious behaviour detection is performed using fuzzy-based secure architecture (FBSA). To evaluate the performance of the proposed approach, comparative analysis is done with conventional schemes using performance parameters such as packet delivery ratio, throughput, total packet forwarding, network overhead, end-to-end delay and percentage of malicious detection.

Integration and Analysis of Neighbor Discovery and Link Quality Estimation in Wireless Sensor Networks

PubMed Central

Radi, Marjan; Dezfouli, Behnam; Abu Bakar, Kamalrulnizam; Abd Razak, Shukor

2014-01-01

Network connectivity and link quality information are the fundamental requirements of wireless sensor network protocols to perform their desired functionality. Most of the existing discovery protocols have only focused on the neighbor discovery problem, while a few number of them provide an integrated neighbor search and link estimation. As these protocols require a careful parameter adjustment before network deployment, they cannot provide scalable and accurate network initialization in large-scale dense wireless sensor networks with random topology. Furthermore, performance of these protocols has not entirely been evaluated yet. In this paper, we perform a comprehensive simulation study on the efficiency of employing adaptive protocols compared to the existing nonadaptive protocols for initializing sensor networks with random topology. In this regard, we propose adaptive network initialization protocols which integrate the initial neighbor discovery with link quality estimation process to initialize large-scale dense wireless sensor networks without requiring any parameter adjustment before network deployment. To the best of our knowledge, this work is the first attempt to provide a detailed simulation study on the performance of integrated neighbor discovery and link quality estimation protocols for initializing sensor networks. This study can help system designers to determine the most appropriate approach for different applications. PMID:24678277

Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set.

PubMed

Lenselink, Eelke B; Ten Dijke, Niels; Bongers, Brandon; Papadatos, George; van Vlijmen, Herman W T; Kowalczyk, Wojtek; IJzerman, Adriaan P; van Westen, Gerard J P

2017-08-14

The increase of publicly available bioactivity data in recent years has fueled and catalyzed research in chemogenomics, data mining, and modeling approaches. As a direct result, over the past few years a multitude of different methods have been reported and evaluated, such as target fishing, nearest neighbor similarity-based methods, and Quantitative Structure Activity Relationship (QSAR)-based protocols. However, such studies are typically conducted on different datasets, using different validation strategies, and different metrics. In this study, different methods were compared using one single standardized dataset obtained from ChEMBL, which is made available to the public, using standardized metrics (BEDROC and Matthews Correlation Coefficient). Specifically, the performance of Naïve Bayes, Random Forests, Support Vector Machines, Logistic Regression, and Deep Neural Networks was assessed using QSAR and proteochemometric (PCM) methods. All methods were validated using both a random split validation and a temporal validation, with the latter being a more realistic benchmark of expected prospective execution. Deep Neural Networks are the top performing classifiers, highlighting the added value of Deep Neural Networks over other more conventional methods. Moreover, the best method ('DNN_PCM') performed significantly better at almost one standard deviation higher than the mean performance. Furthermore, Multi-task and PCM implementations were shown to improve performance over single task Deep Neural Networks. Conversely, target prediction performed almost two standard deviations under the mean performance. Random Forests, Support Vector Machines, and Logistic Regression performed around mean performance. Finally, using an ensemble of DNNs, alongside additional tuning, enhanced the relative performance by another 27% (compared with unoptimized 'DNN_PCM'). Here, a standardized set to test and evaluate different machine learning algorithms in the context of multi-task learning is offered by providing the data and the protocols. Graphical Abstract .

An Energy-Efficient Cluster-Based Vehicle Detection on Road Network Using Intention Numeration Method

PubMed Central

Devasenapathy, Deepa; Kannan, Kathiravan

2015-01-01

The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN) is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate. PMID:25793221

An energy-efficient cluster-based vehicle detection on road network using intention numeration method.

PubMed

Devasenapathy, Deepa; Kannan, Kathiravan

2015-01-01

The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN) is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

Monthly progress report

NASA Technical Reports Server (NTRS)

Moorhead, Robert J., II; Smith, Wayne

1992-01-01

This report is the mid-year report intended for the design concepts for the communication network for the Advanced Solid Rocket Motor (ASRM) facility being built at Yellow Creek near Iuka, MS. The overall network is to include heterogeneous computers, to use various protocols, and to have different bandwidths. Performance consideration must be given to the potential network applications in the network environment. The performance evaluation of X window applications was given the major emphasis in this report. A simulation study using Bones will be included later. This mid-year report has three parts: Part 1 is an investigation of X window traffic using TCP/IP over Ethernet networks; part 2 is a survey study of performance concepts of X window applications with Macintosh computers; and the last part is a tutorial on DECnet protocols. The results of this report should be useful in the design and operation of the ASRM communication network.

Performance evaluation of data center service localization based on virtual resource migration in software defined elastic optical network.

PubMed

Yang, Hui; Zhang, Jie; Ji, Yuefeng; Tan, Yuanlong; Lin, Yi; Han, Jianrui; Lee, Young

2015-09-07

Data center interconnection with elastic optical network is a promising scenario to meet the high burstiness and high-bandwidth requirements of data center services. In our previous work, we implemented cross stratum optimization of optical network and application stratums resources that allows to accommodate data center services. In view of this, this study extends the data center resources to user side to enhance the end-to-end quality of service. We propose a novel data center service localization (DCSL) architecture based on virtual resource migration in software defined elastic data center optical network. A migration evaluation scheme (MES) is introduced for DCSL based on the proposed architecture. The DCSL can enhance the responsiveness to the dynamic end-to-end data center demands, and effectively reduce the blocking probability to globally optimize optical network and application resources. The overall feasibility and efficiency of the proposed architecture are experimentally verified on the control plane of our OpenFlow-based enhanced SDN testbed. The performance of MES scheme under heavy traffic load scenario is also quantitatively evaluated based on DCSL architecture in terms of path blocking probability, provisioning latency and resource utilization, compared with other provisioning scheme.

Modeling and optimization of Quality of Service routing in Mobile Ad hoc Networks

NASA Astrophysics Data System (ADS)

Rafsanjani, Marjan Kuchaki; Fatemidokht, Hamideh; Balas, Valentina Emilia

2016-01-01

Mobile ad hoc networks (MANETs) are a group of mobile nodes that are connected without using a fixed infrastructure. In these networks, nodes communicate with each other by forming a single-hop or multi-hop network. To design effective mobile ad hoc networks, it is important to evaluate the performance of multi-hop paths. In this paper, we present a mathematical model for a routing protocol under energy consumption and packet delivery ratio of multi-hop paths. In this model, we use geometric random graphs rather than random graphs. Our proposed model finds effective paths that minimize the energy consumption and maximizes the packet delivery ratio of the network. Validation of the mathematical model is performed through simulation.

Wireless Infrastructure M2M Network For Distributed Power Grid Monitoring

PubMed Central

Gharavi, Hamid; Hu, Bin

2018-01-01

With the massive integration of distributed renewable energy sources (RESs) into the power system, the demand for timely and reliable network quality monitoring, control, and fault analysis is rapidly growing. Following the successful deployment of Phasor Measurement Units (PMUs) in transmission systems for power monitoring, a new opportunity to utilize PMU measurement data for power quality assessment in distribution grid systems is emerging. The main problem however, is that a distribution grid system does not normally have the support of an infrastructure network. Therefore, the main objective in this paper is to develop a Machine-to-Machine (M2M) communication network that can support wide ranging sensory data, including high rate synchrophasor data for real-time communication. In particular, we evaluate the suitability of the emerging IEEE 802.11ah standard by exploiting its important features, such as classifying the power grid sensory data into different categories according to their traffic characteristics. For performance evaluation we use our hardware in the loop grid communication network testbed to access the performance of the network. PMID:29503505

Wireless Infrastructure M2M Network For Distributed Power Grid Monitoring.

PubMed

Gharavi, Hamid; Hu, Bin

2017-01-01

With the massive integration of distributed renewable energy sources (RESs) into the power system, the demand for timely and reliable network quality monitoring, control, and fault analysis is rapidly growing. Following the successful deployment of Phasor Measurement Units (PMUs) in transmission systems for power monitoring, a new opportunity to utilize PMU measurement data for power quality assessment in distribution grid systems is emerging. The main problem however, is that a distribution grid system does not normally have the support of an infrastructure network. Therefore, the main objective in this paper is to develop a Machine-to-Machine (M2M) communication network that can support wide ranging sensory data, including high rate synchrophasor data for real-time communication. In particular, we evaluate the suitability of the emerging IEEE 802.11ah standard by exploiting its important features, such as classifying the power grid sensory data into different categories according to their traffic characteristics. For performance evaluation we use our hardware in the loop grid communication network testbed to access the performance of the network.

System Analysis for the Huntsville Operation Support Center, Distributed Computer System

NASA Technical Reports Server (NTRS)

Ingels, F. M.; Massey, D.

1985-01-01

HOSC as a distributed computing system, is responsible for data acquisition and analysis during Space Shuttle operations. HOSC also provides computing services for Marshall Space Flight Center's nonmission activities. As mission and nonmission activities change, so do the support functions of HOSC change, demonstrating the need for some method of simulating activity at HOSC in various configurations. The simulation developed in this work primarily models the HYPERchannel network. The model simulates the activity of a steady state network, reporting statistics such as, transmitted bits, collision statistics, frame sequences transmitted, and average message delay. These statistics are used to evaluate such performance indicators as throughout, utilization, and delay. Thus the overall performance of the network is evaluated, as well as predicting possible overload conditions.

Scheduling: A guide for program managers

NASA Technical Reports Server (NTRS)

1994-01-01

The following topics are discussed concerning scheduling: (1) milestone scheduling; (2) network scheduling; (3) program evaluation and review technique; (4) critical path method; (5) developing a network; (6) converting an ugly duckling to a swan; (7) network scheduling problem; (8) (9) network scheduling when resources are limited; (10) multi-program considerations; (11) influence on program performance; (12) line-of-balance technique; (13) time management; (14) recapitulization; and (15) analysis.

Impact of Trust on Security and Performance in Tactical Networks

DTIC Science & Technology

2013-06-01

and reliability . On the other hand, in organizational theory, trust management has viewed trust as a key factor to manage relationships that flourish...environments challenges, these dynamics can hinder accurate and reliable trust evaluation of entities in the network [10], [11]. • Information Network Domain...trustworthy entities. • Social/Cognitive Network Domain: Social scientists, physiologists, and neuroscientists have studied social trust, interpersonal

Implementation of WirelessHART in the NS-2 Simulator and Validation of Its Correctness

PubMed Central

Zand, Pouria; Mathews, Emi; Havinga, Paul; Stojanovski, Spase; Sisinni, Emiliano; Ferrari, Paolo

2014-01-01

One of the first standards in the wireless sensor networks domain, WirelessHART (HART (Highway Addressable Remote Transducer)), was introduced to address industrial process automation and control requirements. This standard can be used as a reference point to evaluate other wireless protocols in the domain of industrial monitoring and control. This makes it worthwhile to set up a reliable WirelessHART simulator in order to achieve that reference point in a relatively easy manner. Moreover, it offers an alternative to expensive testbeds for testing and evaluating the performance of WirelessHART. This paper explains our implementation of WirelessHART in the NS-2 network simulator. According to our knowledge, this is the first implementation that supports the WirelessHART network manager, as well as the whole stack (all OSI (Open Systems Interconnection model) layers) of the WirelessHART standard. It also explains our effort to validate the correctness of our implementation, namely through the validation of the implementation of the WirelessHART stack protocol and of the network manager. We use sniffed traffic from a real WirelessHART testbed installed in the Idrolab plant for these validations. This confirms the validity of our simulator. Empirical analysis shows that the simulated results are nearly comparable to the results obtained from real networks. We also demonstrate the versatility and usability of our implementation by providing some further evaluation results in diverse scenarios. For example, we evaluate the performance of the WirelessHART network by applying incremental interference in a multi-hop network. PMID:24841245

Economic viability of access broadband multiservice networks

NASA Astrophysics Data System (ADS)

Castelli, Francesco; Dammicco, Giacinto; Mocci, Ugo

1995-02-01

In this paper the economic viability of alternative architectures for optical access networks providing broad band services to different subscriber classes in a metropolitan environment, is investigated by a specific tool, NEVE (Network Economic Viability Evaluator), developed for broad band multiservice network planning, service evolutionary scenarios assessment, evaluation of tariff strategies and other actions taken at stimulating the demand growth. As the viability target can be achieved in different ways, different studies can be carried out by NEVE. In the paper some of them are discussed, particularly the ones addressed: to evaluate the impact on viability of alternative service scenarios; to determine the critical mass of broad band subscribers and the critical joint service adoption cost; to evaluate cross subsidiary policies among different subscriber classes and services; to perform sensitivity analysis with respect to variations of demand parameters and tariffs.

Aggregation in Network Models for Transportation Planning

DOT National Transportation Integrated Search

1978-02-01

This report documents research performed on techniques of aggregation applied to network models used in transportation planning. The central objective of this research has been to identify, extend, and evaluate methods of aggregation so as to improve...

Neural network evaluation of reflectometry density profiles for control purposes

NASA Astrophysics Data System (ADS)

Santos, J.; Nunes, F.; Manso, M.; Nunes, I.

1999-01-01

Broadband reflectometry is a diagnostic that is able to measure the density profile with high spatial and temporal resolutions, therefore it can be used to improve the performance of advanced tokamak operation modes and to supplement or correct the magnetics for plasma position control. To perform these tasks real-time processing is needed. Here we present a method that uses a neural network to make a fast evaluation of radial positions for selected density layers. Typical ASDEX Upgrade density profiles were used to generate the simulated network training and test sets. It is shown that the method has the potential to meet the tight timing requirements of control applications with the required accuracy. The network is also able to provide an accurate estimation of the position of density layers below the first density layer which is probed by an O-mode reflectometer, provided that it is trained with a realistic density profile model.

A simplified method of performance indicators development for epidemiological surveillance networks--application to the RESAPATH surveillance network.

PubMed

Sorbe, A; Chazel, M; Gay, E; Haenni, M; Madec, J-Y; Hendrikx, P

2011-06-01

Develop and calculate performance indicators allows to continuously follow the operation of an epidemiological surveillance network. This is an internal evaluation method, implemented by the coordinators in collaboration with all the actors of the network. Its purpose is to detect weak points in order to optimize management. A method for the development of performance indicators of epidemiological surveillance networks was developed in 2004 and was applied to several networks. Its implementation requires a thorough description of the network environment and all its activities to define priority indicators. Since this method is considered to be complex, our objective consisted in developing a simplified approach and applying it to an epidemiological surveillance network. We applied the initial method to a theoretical network model to obtain a list of generic indicators that can be adapted to any surveillance network. We obtained a list of 25 generic performance indicators, intended to be reformulated and described according to the specificities of each network. It was used to develop performance indicators for RESAPATH, an epidemiological surveillance network of antimicrobial resistance in pathogenic bacteria of animal origin in France. This application allowed us to validate the simplified method, its value in terms of practical implementation, and its level of user acceptance. Its ease of use and speed of application compared to the initial method argue in favor of its use on broader scale. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

Mapping of multiple parameter m-health scenarios to mobile WiMAX QoS variables.

PubMed

Alinejad, Ali; Philip, N; Istepanian, R S H

2011-01-01

Multiparameter m-health scenarios with bandwidth demanding requirements will be one of key applications in future 4 G mobile communication systems. These applications will potentially require specific spectrum allocations with higher quality of service requirements. Furthermore, one of the key 4 G technologies targeting m-health will be medical applications based on WiMAX systems. Hence, it is timely to evaluate such multiple parametric m-health scenarios over mobile WiMAX networks. In this paper, we address the preliminary performance analysis of mobile WiMAX network for multiparametric telemedical scenarios. In particular, we map the medical QoS to typical WiMAX QoS parameters to optimise the performance of these parameters in typical m-health scenario. Preliminary performance analyses of the proposed multiparametric scenarios are evaluated to provide essential information for future medical QoS requirements and constraints in these telemedical network environments.

Precipitation forecast using artificial neural networks. An application to the Guadalupe Valley, Baja California, Mexico

NASA Astrophysics Data System (ADS)

Herrera-Oliva, C. S.

2013-05-01

In this work we design and implement a method for the determination of precipitation forecast through the application of an elementary neuronal network (perceptron) to the statistical analysis of the precipitation reported in catalogues. The method is limited mainly by the catalogue length (and, in a smaller degree, by its accuracy). The method performance is measured using grading functions that evaluate a tradeoff between positive and negative aspects of performance. The method is applied to the Guadalupe Valley, Baja California, Mexico. Using consecutive intervals of dt=0.1 year, employing the data of several climatological stations situated in and surrounding this important wine industries zone. We evaluated the performance of different models of ANN, whose variables of entrance are the heights of precipitation. The results obtained were satisfactory, except for exceptional values of rain. Key words: precipitation forecast, artificial neural networks, statistical analysis

Evolving neural networks for strategic decision-making problems.

PubMed

Kohl, Nate; Miikkulainen, Risto

2009-04-01

Evolution of neural networks, or neuroevolution, has been a successful approach to many low-level control problems such as pole balancing, vehicle control, and collision warning. However, certain types of problems-such as those involving strategic decision-making-have remained difficult for neuroevolution to solve. This paper evaluates the hypothesis that such problems are difficult because they are fractured: The correct action varies discontinuously as the agent moves from state to state. A method for measuring fracture using the concept of function variation is proposed and, based on this concept, two methods for dealing with fracture are examined: neurons with local receptive fields, and refinement based on a cascaded network architecture. Experiments in several benchmark domains are performed to evaluate how different levels of fracture affect the performance of neuroevolution methods, demonstrating that these two modifications improve performance significantly. These results form a promising starting point for expanding neuroevolution to strategic tasks.

Impact evaluation of conducted UWB transients on loads in power-line networks

NASA Astrophysics Data System (ADS)

Li, Bing; Månsson, Daniel

2017-09-01

Nowadays, faced with the ever-increasing dependence on diverse electronic devices and systems, the proliferation of potential electromagnetic interference (EMI) becomes a critical threat for reliable operation. A typical issue is the electronics working reliably in power-line networks when exposed to electromagnetic environment. In this paper, we consider a conducted ultra-wideband (UWB) disturbance, as an example of intentional electromagnetic interference (IEMI) source, and perform the impact evaluation at the loads in a network. With the aid of fast Fourier transform (FFT), the UWB transient is characterized in the frequency domain. Based on a modified Baum-Liu-Tesche (BLT) method, the EMI received at the loads, with complex impedance, is computed. Through inverse FFT (IFFT), we obtain time-domain responses of the loads. To evaluate the impact on loads, we employ five common, but important quantifiers, i.e., time-domain peak, total signal energy, peak signal power, peak time rate of change and peak time integral of the pulse. Moreover, to perform a comprehensive analysis, we also investigate the effects of the attributes (capacitive, resistive, or inductive) of other loads connected to the network, the rise time and pulse width of the UWB transient, and the lengths of power lines. It is seen that, for the loads distributed in a network, the impact evaluation of IEMI should be based on the characteristics of the IEMI source, and the network features, such as load impedances, layout, and characteristics of cables.

Ku-band signal design study. [space shuttle orbiter data processing network

NASA Technical Reports Server (NTRS)

Rubin, I.

1978-01-01

Analytical tools, methods and techniques for assessing the design and performance of the space shuttle orbiter data processing system (DPS) are provided. The computer data processing network is evaluated in the key areas of queueing behavior synchronization and network reliability. The structure of the data processing network is described as well as the system operation principles and the network configuration. The characteristics of the computer systems are indicated. System reliability measures are defined and studied. System and network invulnerability measures are computed. Communication path and network failure analysis techniques are included.

New approach to enhance and evaluate the performance of vehicle-infrastructure integration and its communication systems, final report.

DOT National Transportation Integrated Search

2010-09-01

Initial research studied the use of wireless local area networks (WLAN) protocols in Inter-Vehicle Communications : (IVC) environments. The protocols performance was evaluated in terms of measuring throughput, jitter time and : delay time. This re...

Performance Analysis of IIUM Wireless Campus Network

NASA Astrophysics Data System (ADS)

Abd Latif, Suhaimi; Masud, Mosharrof H.; Anwar, Farhat

2013-12-01

International Islamic University Malaysia (IIUM) is one of the leading universities in the world in terms of quality of education that has been achieved due to providing numerous facilities including wireless services to every enrolled student. The quality of this wireless service is controlled and monitored by Information Technology Division (ITD), an ISO standardized organization under the university. This paper aims to investigate the constraints of wireless campus network of IIUM. It evaluates the performance of the IIUM wireless campus network in terms of delay, throughput and jitter. QualNet 5.2 simulator tool has employed to measure these performances of IIUM wireless campus network. The observation from the simulation result could be one of the influencing factors in improving wireless services for ITD and further improvement.

Parallel Evolutionary Optimization for Neuromorphic Network Training

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

Schuman, Catherine D; Disney, Adam; Singh, Susheela

One of the key impediments to the success of current neuromorphic computing architectures is the issue of how best to program them. Evolutionary optimization (EO) is one promising programming technique; in particular, its wide applicability makes it especially attractive for neuromorphic architectures, which can have many different characteristics. In this paper, we explore different facets of EO on a spiking neuromorphic computing model called DANNA. We focus on the performance of EO in the design of our DANNA simulator, and on how to structure EO on both multicore and massively parallel computing systems. We evaluate how our parallel methods impactmore » the performance of EO on Titan, the U.S.'s largest open science supercomputer, and BOB, a Beowulf-style cluster of Raspberry Pi's. We also focus on how to improve the EO by evaluating commonality in higher performing neural networks, and present the result of a study that evaluates the EO performed by Titan.« less

SynchroPhasor Measurements: System Architecture and Performance Evaluation in Supporting Wide-Area Applications

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

Huang, Zhenyu; Dagle, Jeffery E.

2008-07-31

The infrastructure of phasor measurements have evolved over the last two decades from isolated measurement units to networked measurement systems with footprints beyond individual utility companies. This is, to a great extent, a bottom-up self-evolving process except some local systems built by design. Given the number of phasor measurement units (PMUs) in the system is small (currently 70 each in western and eastern interconnections), current phasor network architecture works just fine. However, the architecture will become a bottleneck when large number of PMUs are installed (e.g. >1000~10000). The need for phasor architecture design has yet to be addressed. This papermore » reviews the current phasor networks and investigates future architectures, as related to the efforts undertaken by the North America SynchroPhasor Initiative (NASPI). Then it continues to present staged system tests to evaluate the performance of phasor networks, which is a common practice in the Western Electricity Coordinating Council (WECC) system. This is followed by field measurement evaluation and the implication of phasor quality issues on phasor applications.« less

Strengthening and sustainability of national immunization technical advisory groups (NITAGs) globally: Lessons and recommendations from the founding meeting of the global NITAG network.

PubMed

Adjagba, Alex; MacDonald, Noni E; Ortega-Pérez, Inmaculada; Duclos, Philippe

2017-05-25

National Immunization Technical Advisory Groups (NITAGs) provide independent, evidence-informed advice to assist their governments in immunization policy formation. However, many NITAGs face challenges in fulfilling their roles. Hence the many requests for formation of a network linking NITAGs together so they can learn from each other. To address this request, the Health Policy and Institutional Development (HPID) Center (a WHO Collaborating Center at the Agence de Médecine Préventive - AMP), in collaboration with WHO, organized a meeting in Veyrier-du-Lac, France, on 11 and 12 May 2016, to establish a Global NITAG Network (GNN). The meeting focused on two areas: the requirements for (a) the establishment of a global NITAG collaborative network; and (b) the global assessment/evaluation of the performance of NITAGs. 35 participants from 26 countries reviewed the proposed GNN framework documents and NITAG performance evaluation. Participants recommended that a GNN should be established, agreed on its governance, function, scope and a proposed work plan as well as setting a framework for NITAG evaluation. Copyright © 2017.

Community detection in complex networks by using membrane algorithm

NASA Astrophysics Data System (ADS)

Liu, Chuang; Fan, Linan; Liu, Zhou; Dai, Xiang; Xu, Jiamei; Chang, Baoren

Community detection in complex networks is a key problem of network analysis. In this paper, a new membrane algorithm is proposed to solve the community detection in complex networks. The proposed algorithm is based on membrane systems, which consists of objects, reaction rules, and a membrane structure. Each object represents a candidate partition of a complex network, and the quality of objects is evaluated according to network modularity. The reaction rules include evolutionary rules and communication rules. Evolutionary rules are responsible for improving the quality of objects, which employ the differential evolutionary algorithm to evolve objects. Communication rules implement the information exchanged among membranes. Finally, the proposed algorithm is evaluated on synthetic, real-world networks with real partitions known and the large-scaled networks with real partitions unknown. The experimental results indicate the superior performance of the proposed algorithm in comparison with other experimental algorithms.

Shuttle orbiter S-band payload communications equipment design evaluation

NASA Technical Reports Server (NTRS)

Springett, J. C.; Maronde, R. G.

1979-01-01

The analysis of the design, and the performance assessment of the Orbiter S-band communication equipment are reported. The equipment considered include: network transponder, network signal processor, FM transmitter, FM signal processor, payload interrogator, and payload signal processor.

I/O performance evaluation of a Linux-based network-attached storage device

NASA Astrophysics Data System (ADS)

Sun, Zhaoyan; Dong, Yonggui; Wu, Jinglian; Jia, Huibo; Feng, Guanping

2002-09-01

In a Local Area Network (LAN), clients are permitted to access the files on high-density optical disks via a network server. But the quality of read service offered by the conventional server is not satisfied because of the multiple functions on the server and the overmuch caller. This paper develops a Linux-based Network-Attached Storage (NAS) server. The Operation System (OS), composed of an optimized kernel and a miniaturized file system, is stored in a flash memory. After initialization, the NAS device is connected into the LAN. The administrator and users could configure the access the server through the web page respectively. In order to enhance the quality of access, the management of buffer cache in file system is optimized. Some benchmark programs are peformed to evaluate the I/O performance of the NAS device. Since data recorded in optical disks are usually for reading accesses, our attention is focused on the reading throughput of the device. The experimental results indicate that the I/O performance of our NAS device is excellent.

Power allocation for target detection in radar networks based on low probability of intercept: A cooperative game theoretical strategy

NASA Astrophysics Data System (ADS)

Shi, Chenguang; Salous, Sana; Wang, Fei; Zhou, Jianjiang

2017-08-01

Distributed radar network systems have been shown to have many unique features. Due to their advantage of signal and spatial diversities, radar networks are attractive for target detection. In practice, the netted radars in radar networks are supposed to maximize their transmit power to achieve better detection performance, which may be in contradiction with low probability of intercept (LPI). Therefore, this paper investigates the problem of adaptive power allocation for radar networks in a cooperative game-theoretic framework such that the LPI performance can be improved. Taking into consideration both the transmit power constraints and the minimum signal to interference plus noise ratio (SINR) requirement of each radar, a cooperative Nash bargaining power allocation game based on LPI is formulated, whose objective is to minimize the total transmit power by optimizing the power allocation in radar networks. First, a novel SINR-based network utility function is defined and utilized as a metric to evaluate power allocation. Then, with the well-designed network utility function, the existence and uniqueness of the Nash bargaining solution are proved analytically. Finally, an iterative Nash bargaining algorithm is developed that converges quickly to a Pareto optimal equilibrium for the cooperative game. Numerical simulations and theoretic analysis are provided to evaluate the effectiveness of the proposed algorithm.

Design, Implementation, and Evaluation of a Virtual Shared Memory System in a Multi-Transputer Network.

DTIC Science & Technology

1987-12-01

Synchronization and Data Passing Mechanism ........ 50 4. System Shut Down .................................................................. 51 5...high performance, fault tolerance, and extensibility. These features are attained by synchronizing and coordinating the dis- tributed multicomputer... synchronizing all processors in the network. In a multitransputer network, processes that communicate with each other do so synchronously . This makes

Evaluation Study of a Wireless Multimedia Traffic-Oriented Network Model

NASA Astrophysics Data System (ADS)

Vasiliadis, D. C.; Rizos, G. E.; Vassilakis, C.

2008-11-01

In this paper, a wireless multimedia traffic-oriented network scheme over a fourth generation system (4-G) is presented and analyzed. We conducted an extensive evaluation study for various mobility configurations in order to incorporate the behavior of the IEEE 802.11b standard over a test-bed wireless multimedia network model. In this context, the Quality of Services (QoS) over this network is vital for providing a reliable high-bandwidth platform for data-intensive sources like video streaming. Therefore, the main issues concerned in terms of QoS were the metrics for bandwidth of both dropped and lost packets and their mean packet delay under various traffic conditions. Finally, we used a generic distance-vector routing protocol which was based on an implementation of Distributed Bellman-Ford algorithm. The performance of the test-bed network model has been evaluated by using the simulation environment of NS-2.

Scalable and reusable emulator for evaluating the performance of SS7 networks

NASA Astrophysics Data System (ADS)

Lazar, Aurel A.; Tseng, Kent H.; Lim, Koon Seng; Choe, Winston

1994-04-01

A scalable and reusable emulator was designed and implemented for studying the behavior of SS7 networks. The emulator design was largely based on public domain software. It was developed on top of an environment supported by PVM, the Parallel Virtual Machine, and managed by OSIMIS-the OSI Management Information Service platform. The emulator runs on top of a commercially available ATM LAN interconnecting engineering workstations. As a case study for evaluating the emulator, the behavior of the Singapore National SS7 Network under fault and unbalanced loading conditions was investigated.

Performance evaluation of modulation and multiple access schemes in ultraviolet optical wireless connections for two atmosphere thickness cases.

PubMed

Raptis, Nikos; Pikasis, Evangelos; Syvridis, Dimitris

2016-08-01

The exploitation of optical wireless communication channels in a non-line-of-sight regime is studied for point-to-point and networking configurations considering the use of light-emitting diodes. Two environments with different scattering center densities are considered, assuming operation at 265 nm. The bit error rate performance of both pulsed and multicarrier modulation schemes is examined, using numerical approaches. In the networking scenario, a central node only receives data, one node transmits useful data, and the rest of them act as interferers. The performance of the desirable node's transmissions is evaluated. The access to the medium is controlled by a code division multiple access scheme.

Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches.

PubMed

Crichton, Gamal; Guo, Yufan; Pyysalo, Sampo; Korhonen, Anna

2018-05-21

Link prediction in biomedical graphs has several important applications including predicting Drug-Target Interactions (DTI), Protein-Protein Interaction (PPI) prediction and Literature-Based Discovery (LBD). It can be done using a classifier to output the probability of link formation between nodes. Recently several works have used neural networks to create node representations which allow rich inputs to neural classifiers. Preliminary works were done on this and report promising results. However they did not use realistic settings like time-slicing, evaluate performances with comprehensive metrics or explain when or why neural network methods outperform. We investigated how inputs from four node representation algorithms affect performance of a neural link predictor on random- and time-sliced biomedical graphs of real-world sizes (∼ 6 million edges) containing information relevant to DTI, PPI and LBD. We compared the performance of the neural link predictor to those of established baselines and report performance across five metrics. In random- and time-sliced experiments when the neural network methods were able to learn good node representations and there was a negligible amount of disconnected nodes, those approaches outperformed the baselines. In the smallest graph (∼ 15,000 edges) and in larger graphs with approximately 14% disconnected nodes, baselines such as Common Neighbours proved a justifiable choice for link prediction. At low recall levels (∼ 0.3) the approaches were mostly equal, but at higher recall levels across all nodes and average performance at individual nodes, neural network approaches were superior. Analysis showed that neural network methods performed well on links between nodes with no previous common neighbours; potentially the most interesting links. Additionally, while neural network methods benefit from large amounts of data, they require considerable amounts of computational resources to utilise them. Our results indicate that when there is enough data for the neural network methods to use and there are a negligible amount of disconnected nodes, those approaches outperform the baselines. At low recall levels the approaches are mostly equal but at higher recall levels and average performance at individual nodes, neural network approaches are superior. Performance at nodes without common neighbours which indicate more unexpected and perhaps more useful links account for this.

On the MAC/network/energy performance evaluation of Wireless Sensor Networks: Contrasting MPH, AODV, DSR and ZTR routing protocols.

PubMed

Del-Valle-Soto, Carolina; Mex-Perera, Carlos; Orozco-Lugo, Aldo; Lara, Mauricio; Galván-Tejada, Giselle M; Olmedo, Oscar

2014-12-02

Wireless Sensor Networks deliver valuable information for long periods, then it is desirable to have optimum performance, reduced delays, low overhead, and reliable delivery of information. In this work, proposed metrics that influence energy consumption are used for a performance comparison among our proposed routing protocol, called Multi-Parent Hierarchical (MPH), the well-known protocols for sensor networks, Ad hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Zigbee Tree Routing (ZTR), all of them working with the IEEE 802.15.4 MAC layer. Results show how some communication metrics affect performance, throughput, reliability and energy consumption. It can be concluded that MPH is an efficient protocol since it reaches the best performance against the other three protocols under evaluation, such as 19.3% reduction of packet retransmissions, 26.9% decrease of overhead, and 41.2% improvement on the capacity of the protocol for recovering the topology from failures with respect to AODV protocol. We implemented and tested MPH in a real network of 99 nodes during ten days and analyzed parameters as number of hops, connectivity and delay, in order to validate our Sensors 2014, 14 22812 simulator and obtain reliable results. Moreover, an energy model of CC2530 chip is proposed and used for simulations of the four aforementioned protocols, showing that MPH has 15.9% reduction of energy consumption with respect to AODV, 13.7% versus DSR, and 5% against ZTR.

Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

NASA Technical Reports Server (NTRS)

Jorgensen, Charles C.

1997-01-01

A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

A Flexible System for Simulating Aeronautical Telecommunication Network

NASA Technical Reports Server (NTRS)

Maly, Kurt; Overstreet, C. M.; Andey, R.

1998-01-01

At Old Dominion University, we have built Aeronautical Telecommunication Network (ATN) Simulator with NASA being the fund provider. It provides a means to evaluate the impact of modified router scheduling algorithms on the network efficiency, to perform capacity studies on various network topologies and to monitor and study various aspects of ATN through graphical user interface (GUI). In this paper we describe briefly about the proposed ATN model and our abstraction of this model. Later we describe our simulator architecture highlighting some of the design specifications, scheduling algorithms and user interface. At the end, we have provided the results of performance studies on this simulator.

SANDS: an architecture for clinical decision support in a National Health Information Network.

PubMed

Wright, Adam; Sittig, Dean F

2007-10-11

A new architecture for clinical decision support called SANDS (Service-oriented Architecture for NHIN Decision Support) is introduced and its performance evaluated. The architecture provides a method for performing clinical decision support across a network, as in a health information exchange. Using the prototype we demonstrated that, first, a number of useful types of decision support can be carried out using our architecture; and, second, that the architecture exhibits desirable reliability and performance characteristics.

A Novel Approach to Noise-Filtering Based on a Gain-Scheduling Neural Network Architecture

NASA Technical Reports Server (NTRS)

Troudet, T.; Merrill, W.

1994-01-01

A gain-scheduling neural network architecture is proposed to enhance the noise-filtering efficiency of feedforward neural networks, in terms of both nominal performance and robustness. The synergistic benefits of the proposed architecture are demonstrated and discussed in the context of the noise-filtering of signals that are typically encountered in aerospace control systems. The synthesis of such a gain-scheduled neurofiltering provides the robustness of linear filtering, while preserving the nominal performance advantage of conventional nonlinear neurofiltering. Quantitative performance and robustness evaluations are provided for the signal processing of pitch rate responses to typical pilot command inputs for a modern fighter aircraft model.

Application of Frequency of Detection Methods in Design and Optimization of the INL Site Ambient Air Monitoring Network

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

Rood, Arthur S.; Sondrup, A. Jeffrey

This report presents an evaluation of a hypothetical INL Site monitoring network and the existing INL air monitoring network using frequency of detection methods. The hypothetical network was designed to address the requirement in 40 CFR Part 61, Subpart H (2006) that “emissions of radionuclides to ambient air from U.S. DOE facilities shall not exceed those amounts that would cause any member of the public to receive in any year an effective dose equivalent exceeding 10 mrem/year.” To meet the requirement for monitoring only, “radionuclide releases that would result in an effective dose of 10% of the standard shall bemore » readily detectable and distinguishable from background.” Thus, the hypothetical network consists of air samplers placed at residence locations that surround INL and at other locations where onsite livestock grazing takes place. Two exposure scenarios were used in this evaluation: a resident scenario and a shepherd/rancher scenario. The resident was assumed to be continuously present at their residence while the shepherd/rancher was assumed to be present 24-hours at a fixed location on the grazing allotment. Important radionuclides were identified from annual INL radionuclide National Emission Standards for Hazardous Pollutants reports. Important radionuclides were defined as those that potentially contribute 1% or greater to the annual total dose at the radionuclide National Emission Standards for Hazardous Pollutants maximally exposed individual location and include H-3, Am-241, Pu-238, Pu 239, Cs-137, Sr-90, and I-131. For this evaluation, the network performance objective was set at achieving a frequency of detection greater than or equal to 95%. Results indicated that the hypothetical network for the resident scenario met all performance objectives for H-3 and I-131 and most performance objectives for Cs-137 and Sr-90. However, all actinides failed to meet the performance objectives for most sources. The shepherd/rancher scenario showed that air samplers placed around the facilities every 22.5 degrees were very effective in detecting releases, but this arrangement is not practical or cost effective. However, it was shown that a few air samplers placed in the prevailing wind direction around each facility could achieve the performance objective of a frequency of detection greater than or equal to 95% for the shepherd/rancher scenario. The results also indicate some of the current sampler locations have little or no impact on the network frequency of detection and could be removed from the network with no appreciable deterioration of performance. Results show that with some slight modifications to the existing network (i.e., additional samplers added north and south of the Materials and Fuels Complex and ineffective samplers removed), the network would achieve performance objectives for all sources for both the resident and shepherd/rancher scenario.« less

Evaluating Research and Impact: A Bibliometric Analysis of Research by the NIH/NIAID HIV/AIDS Clinical Trials Networks

PubMed Central

Rosas, Scott R.; Kagan, Jonathan M.; Schouten, Jeffrey T.; Slack, Perry A.; Trochim, William M. K.

2011-01-01

Evaluative bibliometrics uses advanced techniques to assess the impact of scholarly work in the context of other scientific work and usually compares the relative scientific contributions of research groups or institutions. Using publications from the National Institute of Allergy and Infectious Diseases (NIAID) HIV/AIDS extramural clinical trials networks, we assessed the presence, performance, and impact of papers published in 2006–2008. Through this approach, we sought to expand traditional bibliometric analyses beyond citation counts to include normative comparisons across journals and fields, visualization of co-authorship across the networks, and assess the inclusion of publications in reviews and syntheses. Specifically, we examined the research output of the networks in terms of the a) presence of papers in the scientific journal hierarchy ranked on the basis of journal influence measures, b) performance of publications on traditional bibliometric measures, and c) impact of publications in comparisons with similar publications worldwide, adjusted for journals and fields. We also examined collaboration and interdisciplinarity across the initiative, through network analysis and modeling of co-authorship patterns. Finally, we explored the uptake of network produced publications in research reviews and syntheses. Overall, the results suggest the networks are producing highly recognized work, engaging in extensive interdisciplinary collaborations, and having an impact across several areas of HIV-related science. The strengths and limitations of the approach for evaluation and monitoring research initiatives are discussed. PMID:21394198

High-Throughput and Low-Latency Network Communication with NetIO

NASA Astrophysics Data System (ADS)

Schumacher, Jörn; Plessl, Christian; Vandelli, Wainer

2017-10-01

HPC network technologies like Infiniband, TrueScale or OmniPath provide low- latency and high-throughput communication between hosts, which makes them attractive options for data-acquisition systems in large-scale high-energy physics experiments. Like HPC networks, DAQ networks are local and include a well specified number of systems. Unfortunately traditional network communication APIs for HPC clusters like MPI or PGAS exclusively target the HPC community and are not suited well for DAQ applications. It is possible to build distributed DAQ applications using low-level system APIs like Infiniband Verbs, but it requires a non-negligible effort and expert knowledge. At the same time, message services like ZeroMQ have gained popularity in the HEP community. They make it possible to build distributed applications with a high-level approach and provide good performance. Unfortunately, their usage usually limits developers to TCP/IP- based networks. While it is possible to operate a TCP/IP stack on top of Infiniband and OmniPath, this approach may not be very efficient compared to a direct use of native APIs. NetIO is a simple, novel asynchronous message service that can operate on Ethernet, Infiniband and similar network fabrics. In this paper the design and implementation of NetIO is presented and described, and its use is evaluated in comparison to other approaches. NetIO supports different high-level programming models and typical workloads of HEP applications. The ATLAS FELIX project [1] successfully uses NetIO as its central communication platform. The architecture of NetIO is described in this paper, including the user-level API and the internal data-flow design. The paper includes a performance evaluation of NetIO including throughput and latency measurements. The performance is compared against the state-of-the- art ZeroMQ message service. Performance measurements are performed in a lab environment with Ethernet and FDR Infiniband networks.

Performance evaluation of coherent Ising machines against classical neural networks

NASA Astrophysics Data System (ADS)

Haribara, Yoshitaka; Ishikawa, Hitoshi; Utsunomiya, Shoko; Aihara, Kazuyuki; Yamamoto, Yoshihisa

2017-12-01

The coherent Ising machine is expected to find a near-optimal solution in various combinatorial optimization problems, which has been experimentally confirmed with optical parametric oscillators and a field programmable gate array circuit. The similar mathematical models were proposed three decades ago by Hopfield et al in the context of classical neural networks. In this article, we compare the computational performance of both models.

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

DTIC Science & Technology

2010-06-01

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

Use of neural networks to model complex immunogenetic associations of disease: human leukocyte antigen impact on the progression of human immunodeficiency virus infection.

PubMed

Ioannidis, J P; McQueen, P G; Goedert, J J; Kaslow, R A

1998-03-01

Complex immunogenetic associations of disease involving a large number of gene products are difficult to evaluate with traditional statistical methods and may require complex modeling. The authors evaluated the performance of feed-forward backpropagation neural networks in predicting rapid progression to acquired immunodeficiency syndrome (AIDS) for patients with human immunodeficiency virus (HIV) infection on the basis of major histocompatibility complex variables. Networks were trained on data from patients from the Multicenter AIDS Cohort Study (n = 139) and then validated on patients from the DC Gay cohort (n = 102). The outcome of interest was rapid disease progression, defined as progression to AIDS in <6 years from seroconversion. Human leukocyte antigen (HLA) variables were selected as network inputs with multivariate regression and a previously described algorithm selecting markers with extreme point estimates for progression risk. Network performance was compared with that of logistic regression. Networks with 15 HLA inputs and a single hidden layer of five nodes achieved a sensitivity of 87.5% and specificity of 95.6% in the training set, vs. 77.0% and 76.9%, respectively, achieved by logistic regression. When validated on the DC Gay cohort, networks averaged a sensitivity of 59.1% and specificity of 74.3%, vs. 53.1% and 61.4%, respectively, for logistic regression. Neural networks offer further support to the notion that HIV disease progression may be dependent on complex interactions between different class I and class II alleles and transporters associated with antigen processing variants. The effect in the current models is of moderate magnitude, and more data as well as other host and pathogen variables may need to be considered to improve the performance of the models. Artificial intelligence methods may complement linear statistical methods for evaluating immunogenetic associations of disease.

Implementation and Performance Evaluation Using the Fuzzy Network Balanced Scorecard

ERIC Educational Resources Information Center

Tseng, Ming-Lang

2010-01-01

The balanced scorecard (BSC) is a multi-criteria evaluation concept that highlights the importance of performance measurement. However, although there is an abundance of literature on the BSC framework, there is a scarcity of literature regarding how the framework with dependence and interactive relationships should be properly implemented in…

An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network.

PubMed

Vimalarani, C; Subramanian, R; Sivanandam, S N

2016-01-01

Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

Traffic placement policies for a multi-band network

NASA Technical Reports Server (NTRS)

Maly, Kurt J.; Foudriat, E. C.; Game, David; Mukkamala, R.; Overstreet, C. Michael

1990-01-01

Recently protocols were introduced that enable the integration of synchronous traffic (voice or video) and asynchronous traffic (data) and extend the size of local area networks without loss in speed or capacity. One of these is DRAMA, a multiband protocol based on broadband technology. It provides dynamic allocation of bandwidth among clusters of nodes in the total network. A number of traffic placement policies for such networks are proposed and evaluated. Metrics used for performance evaluation include average network access delay, degree of fairness of access among the nodes, and network throughput. The feasibility of the DRAMA protocol is established through simulation studies. DRAMA provides effective integration of synchronous and asychronous traffic due to its ability to separate traffic types. Under the suggested traffic placement policies, the DRAMA protocol is shown to handle diverse loads, mixes of traffic types, and numbers of nodes, as well as modifications to the network structure and momentary traffic overloads.

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

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

Potok, Thomas E; Schuman, Catherine D; Young, Steven R

Current Deep Learning models use highly optimized convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers with a fairly simple layered network topology, i.e., highly connected layers, without intra-layer connections. Complex topologies have been proposed, but are intractable to train on current systems. Building the topologies of the deep learning network requires hand tuning, and implementing the network in hardware is expensive in both cost and power. In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC) to automatically determinemore » network topology, and neuromorphic computing for a low-power hardware implementation. Due to input size limitations of current quantum computers we use the MNIST dataset for our evaluation. The results show the possibility of using the three architectures in tandem to explore complex deep learning networks that are untrainable using a von Neumann architecture. We show that a quantum computer can find high quality values of intra-layer connections and weights, while yielding a tractable time result as the complexity of the network increases; a high performance computer can find optimal layer-based topologies; and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware. This represents a new capability that is not feasible with current von Neumann architecture. It potentially enables the ability to solve very complicated problems unsolvable with current computing technologies.« less

FSM-F: Finite State Machine Based Framework for Denial of Service and Intrusion Detection in MANET.

PubMed

N Ahmed, Malik; Abdullah, Abdul Hanan; Kaiwartya, Omprakash

2016-01-01

Due to the continuous advancements in wireless communication in terms of quality of communication and affordability of the technology, the application area of Mobile Adhoc Networks (MANETs) significantly growing particularly in military and disaster management. Considering the sensitivity of the application areas, security in terms of detection of Denial of Service (DoS) and intrusion has become prime concern in research and development in the area. The security systems suggested in the past has state recognition problem where the system is not able to accurately identify the actual state of the network nodes due to the absence of clear definition of states of the nodes. In this context, this paper proposes a framework based on Finite State Machine (FSM) for denial of service and intrusion detection in MANETs. In particular, an Interruption Detection system for Adhoc On-demand Distance Vector (ID-AODV) protocol is presented based on finite state machine. The packet dropping and sequence number attacks are closely investigated and detection systems for both types of attacks are designed. The major functional modules of ID-AODV includes network monitoring system, finite state machine and attack detection model. Simulations are carried out in network simulator NS-2 to evaluate the performance of the proposed framework. A comparative evaluation of the performance is also performed with the state-of-the-art techniques: RIDAN and AODV. The performance evaluations attest the benefits of proposed framework in terms of providing better security for denial of service and intrusion detection attacks.

A holistic approach to ZigBee performance enhancement for home automation networks.

PubMed

Betzler, August; Gomez, Carles; Demirkol, Ilker; Paradells, Josep

2014-08-14

Wireless home automation networks are gaining importance for smart homes. In this ambit, ZigBee networks play an important role. The ZigBee specification defines a default set of protocol stack parameters and mechanisms that is further refined by the ZigBee Home Automation application profile. In a holistic approach, we analyze how the network performance is affected with the tuning of parameters and mechanisms across multiple layers of the ZigBee protocol stack and investigate possible performance gains by implementing and testing alternative settings. The evaluations are carried out in a testbed of 57 TelosB motes. The results show that considerable performance improvements can be achieved by using alternative protocol stack configurations. From these results, we derive two improved protocol stack configurations for ZigBee wireless home automation networks that are validated in various network scenarios. In our experiments, these improved configurations yield a relative packet delivery ratio increase of up to 33.6%, a delay decrease of up to 66.6% and an improvement of the energy efficiency for battery powered devices of up to 48.7%, obtainable without incurring any overhead to the network.

A Holistic Approach to ZigBee Performance Enhancement for Home Automation Networks

PubMed Central

Betzler, August; Gomez, Carles; Demirkol, Ilker; Paradells, Josep

2014-01-01

Wireless home automation networks are gaining importance for smart homes. In this ambit, ZigBee networks play an important role. The ZigBee specification defines a default set of protocol stack parameters and mechanisms that is further refined by the ZigBee Home Automation application profile. In a holistic approach, we analyze how the network performance is affected with the tuning of parameters and mechanisms across multiple layers of the ZigBee protocol stack and investigate possible performance gains by implementing and testing alternative settings. The evaluations are carried out in a testbed of 57 TelosB motes. The results show that considerable performance improvements can be achieved by using alternative protocol stack configurations. From these results, we derive two improved protocol stack configurations for ZigBee wireless home automation networks that are validated in various network scenarios. In our experiments, these improved configurations yield a relative packet delivery ratio increase of up to 33.6%, a delay decrease of up to 66.6% and an improvement of the energy efficiency for battery powered devices of up to 48.7%, obtainable without incurring any overhead to the network. PMID:25196004

Re-Evaluation of the AASHTO-Flexible Pavement Design Equation with Neural Network Modeling

PubMed Central

Tiğdemir, Mesut

2014-01-01

Here we establish that equivalent single-axle loads values can be estimated using artificial neural networks without the complex design equality of American Association of State Highway and Transportation Officials (AASHTO). More importantly, we find that the neural network model gives the coefficients to be able to obtain the actual load values using the AASHTO design values. Thus, those design traffic values that might result in deterioration can be better calculated using the neural networks model than with the AASHTO design equation. The artificial neural network method is used for this purpose. The existing AASHTO flexible pavement design equation does not currently predict the pavement performance of the strategic highway research program (Long Term Pavement Performance studies) test sections very accurately, and typically over-estimates the number of equivalent single axle loads needed to cause a measured loss of the present serviceability index. Here we aimed to demonstrate that the proposed neural network model can more accurately represent the loads values data, compared against the performance of the AASHTO formula. It is concluded that the neural network may be an appropriate tool for the development of databased-nonparametric models of pavement performance. PMID:25397962

Re-evaluation of the AASHTO-flexible pavement design equation with neural network modeling.

PubMed

Tiğdemir, Mesut

2014-01-01

Here we establish that equivalent single-axle loads values can be estimated using artificial neural networks without the complex design equality of American Association of State Highway and Transportation Officials (AASHTO). More importantly, we find that the neural network model gives the coefficients to be able to obtain the actual load values using the AASHTO design values. Thus, those design traffic values that might result in deterioration can be better calculated using the neural networks model than with the AASHTO design equation. The artificial neural network method is used for this purpose. The existing AASHTO flexible pavement design equation does not currently predict the pavement performance of the strategic highway research program (Long Term Pavement Performance studies) test sections very accurately, and typically over-estimates the number of equivalent single axle loads needed to cause a measured loss of the present serviceability index. Here we aimed to demonstrate that the proposed neural network model can more accurately represent the loads values data, compared against the performance of the AASHTO formula. It is concluded that the neural network may be an appropriate tool for the development of databased-nonparametric models of pavement performance.

Sequential defense against random and intentional attacks in complex networks.

PubMed

Chen, Pin-Yu; Cheng, Shin-Ming

2015-02-01

Network robustness against attacks is one of the most fundamental researches in network science as it is closely associated with the reliability and functionality of various networking paradigms. However, despite the study on intrinsic topological vulnerabilities to node removals, little is known on the network robustness when network defense mechanisms are implemented, especially for networked engineering systems equipped with detection capabilities. In this paper, a sequential defense mechanism is first proposed in complex networks for attack inference and vulnerability assessment, where the data fusion center sequentially infers the presence of an attack based on the binary attack status reported from the nodes in the network. The network robustness is evaluated in terms of the ability to identify the attack prior to network disruption under two major attack schemes, i.e., random and intentional attacks. We provide a parametric plug-in model for performance evaluation on the proposed mechanism and validate its effectiveness and reliability via canonical complex network models and real-world large-scale network topology. The results show that the sequential defense mechanism greatly improves the network robustness and mitigates the possibility of network disruption by acquiring limited attack status information from a small subset of nodes in the network.

Artificial Neural Network for the Prediction of Chromosomal Abnormalities in Azoospermic Males.

PubMed

Akinsal, Emre Can; Haznedar, Bulent; Baydilli, Numan; Kalinli, Adem; Ozturk, Ahmet; Ekmekçioğlu, Oğuz

2018-02-04

To evaluate whether an artifical neural network helps to diagnose any chromosomal abnormalities in azoospermic males. The data of azoospermic males attending to a tertiary academic referral center were evaluated retrospectively. Height, total testicular volume, follicle stimulating hormone, luteinising hormone, total testosterone and ejaculate volume of the patients were used for the analyses. In artificial neural network, the data of 310 azoospermics were used as the education and 115 as the test set. Logistic regression analyses and discriminant analyses were performed for statistical analyses. The tests were re-analysed with a neural network. Both logistic regression analyses and artificial neural network predicted the presence or absence of chromosomal abnormalities with more than 95% accuracy. The use of artificial neural network model has yielded satisfactory results in terms of distinguishing patients whether they have any chromosomal abnormality or not.

Performance evaluation of time-aware enhanced software defined networking (TeSDN) for elastic data center optical interconnection.

PubMed

Yang, Hui; Zhang, Jie; Zhao, Yongli; Ji, Yuefeng; Li, Hui; Lin, Yi; Li, Gang; Han, Jianrui; Lee, Young; Ma, Teng

2014-07-28

Data center interconnection with elastic optical networks is a promising scenario to meet the high burstiness and high-bandwidth requirements of data center services. We previously implemented enhanced software defined networking over elastic optical network for data center application [Opt. Express 21, 26990 (2013)]. On the basis of it, this study extends to consider the time-aware data center service scheduling with elastic service time and service bandwidth according to the various time sensitivity requirements. A novel time-aware enhanced software defined networking (TeSDN) architecture for elastic data center optical interconnection has been proposed in this paper, by introducing a time-aware resources scheduling (TaRS) scheme. The TeSDN can accommodate the data center services with required QoS considering the time dimensionality, and enhance cross stratum optimization of application and elastic optical network stratums resources based on spectrum elasticity, application elasticity and time elasticity. The overall feasibility and efficiency of the proposed architecture is experimentally verified on our OpenFlow-based testbed. The performance of TaRS scheme under heavy traffic load scenario is also quantitatively evaluated based on TeSDN architecture in terms of blocking probability and resource occupation rate.

Pliable Cognitive MAC for Heterogeneous Adaptive Cognitive Radio Sensor Networks.

PubMed

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

2016-01-01

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

Pliable Cognitive MAC for Heterogeneous Adaptive Cognitive Radio Sensor Networks

PubMed Central

Ali, Borhanuddin Mohd; Sali, Aduwati

2016-01-01

The rapid expansion of wireless monitoring and surveillance applications in several domains reinforces the trend of exploiting emerging technologies such as the cognitive radio. However, these technologies have to adjust their working concepts to consider the common characteristics of conventional wireless sensor networks (WSNs). The cognitive radio sensor network (CRSN), still an immature technology, has to deal with new networks that might have different types of data, traffic patterns, or quality of service (QoS) requirements. In this paper, we design and model a new cognitive radio-based medium access control (MAC) algorithm dealing with the heterogeneous nature of the developed networks in terms of either the traffic pattern or the required QoS for the node applications. The proposed algorithm decreases the consumed power on several fronts, provides satisfactory levels of latency and spectrum utilization with efficient scheduling, and manages the radio resources for various traffic conditions. An intensive performance evaluation is conducted to study the impact of key parameters such as the channel idle time length, node density, and the number of available channels. The performance evaluation of the proposed algorithm shows a better performance than the comparable protocols. Moreover, the results manifest that the proposed algorithm is suitable for real time monitoring applications. PMID:27257964

Performance Evaluation Modeling of Network Sensors

NASA Technical Reports Server (NTRS)

Clare, Loren P.; Jennings, Esther H.; Gao, Jay L.

2003-01-01

Substantial benefits are promised by operating many spatially separated sensors collectively. Such systems are envisioned to consist of sensor nodes that are connected by a communications network. A simulation tool is being developed to evaluate the performance of networked sensor systems, incorporating such metrics as target detection probabilities, false alarms rates, and classification confusion probabilities. The tool will be used to determine configuration impacts associated with such aspects as spatial laydown, and mixture of different types of sensors (acoustic, seismic, imaging, magnetic, RF, etc.), and fusion architecture. The QualNet discrete-event simulation environment serves as the underlying basis for model development and execution. This platform is recognized for its capabilities in efficiently simulating networking among mobile entities that communicate via wireless media. We are extending QualNet's communications modeling constructs to capture the sensing aspects of multi-target sensing (analogous to multiple access communications), unimodal multi-sensing (broadcast), and multi-modal sensing (multiple channels and correlated transmissions). Methods are also being developed for modeling the sensor signal sources (transmitters), signal propagation through the media, and sensors (receivers) that are consistent with the discrete event paradigm needed for performance determination of sensor network systems. This work is supported under the Microsensors Technical Area of the Army Research Laboratory (ARL) Advanced Sensors Collaborative Technology Alliance.

Detecting ‘Wrong Blood in Tube’ Errors: Evaluation of a Bayesian Network Approach

PubMed Central

Doctor, Jason N.; Strylewicz, Greg

2010-01-01

Objective In an effort to address the problem of laboratory errors, we develop and evaluate a method to detect mismatched specimens from nationally collected blood laboratory data in two experiments. Methods In Experiment 1 and 2 using blood labs from National Health and Nutrition Examination Survey (NHANES) and values derived from the Diabetes Prevention Program (DPP) respectively, a proportion of glucose and HbA1c specimens were randomly mismatched. A Bayesian network that encoded probabilistic relationships among analytes was used to predict mismatches. In Experiment 1 the performance of the network was compared against existing error detection software. In Experiment 2 the network was compared against 11 human experts recruited from the American Academy of Clinical Chemists. Results were compared via area under the receiver-operating characteristics curves (AUCs) and with agreement statistics. Results In Experiment 1 the network was most predictive of mismatches that produced clinically significant discrepancies between true and mismatched scores ((AUC of 0.87 (±0.04) for HbA1c and 0.83 (±0.02) for glucose), performed well in identifying errors among those self-reporting diabetes (N = 329) (AUC = 0.79 (± 0.02)) and performed significantly better than the established approach it was tested against (in all cases p < .0.05). In Experiment 2 it performed better (and in no case worse) than 7 of the 11 human experts. Average percent agreement was 0.79. and Kappa (κ) was 0.59, between experts and the Bayesian network. Conclusions Bayesian network can accurately identify mismatched specimens. The algorithm is best at identifying mismatches that result in a clinically significant magnitude of error. PMID:20566275

CommWalker: correctly evaluating modules in molecular networks in light of annotation bias.

PubMed

Luecken, M D; Page, M J T; Crosby, A J; Mason, S; Reinert, G; Deane, C M

2018-03-15

Detecting novel functional modules in molecular networks is an important step in biological research. In the absence of gold standard functional modules, functional annotations are often used to verify whether detected modules/communities have biological meaning. However, as we show, the uneven distribution of functional annotations means that such evaluation methods favor communities of well-studied proteins. We propose a novel framework for the evaluation of communities as functional modules. Our proposed framework, CommWalker, takes communities as inputs and evaluates them in their local network environment by performing short random walks. We test CommWalker's ability to overcome annotation bias using input communities from four community detection methods on two protein interaction networks. We find that modules accepted by CommWalker are similarly co-expressed as those accepted by current methods. Crucially, CommWalker performs well not only in well-annotated regions, but also in regions otherwise obscured by poor annotation. CommWalker community prioritization both faithfully captures well-validated communities and identifies functional modules that may correspond to more novel biology. The CommWalker algorithm is freely available at opig.stats.ox.ac.uk/resources or as a docker image on the Docker Hub at hub.docker.com/r/lueckenmd/commwalker/. deane@stats.ox.ac.uk. Supplementary data are available at Bioinformatics online.

A class of finite-time dual neural networks for solving quadratic programming problems and its k-winners-take-all application.

PubMed

Li, Shuai; Li, Yangming; Wang, Zheng

2013-03-01

This paper presents a class of recurrent neural networks to solve quadratic programming problems. Different from most existing recurrent neural networks for solving quadratic programming problems, the proposed neural network model converges in finite time and the activation function is not required to be a hard-limiting function for finite convergence time. The stability, finite-time convergence property and the optimality of the proposed neural network for solving the original quadratic programming problem are proven in theory. Extensive simulations are performed to evaluate the performance of the neural network with different parameters. In addition, the proposed neural network is applied to solving the k-winner-take-all (k-WTA) problem. Both theoretical analysis and numerical simulations validate the effectiveness of our method for solving the k-WTA problem. Copyright © 2012 Elsevier Ltd. All rights reserved.

Potential of dynamic spectrum allocation in LTE macro networks

NASA Astrophysics Data System (ADS)

Hoffmann, H.; Ramachandra, P.; Kovács, I. Z.; Jorguseski, L.; Gunnarsson, F.; Kürner, T.

2015-11-01

In recent years Mobile Network Operators (MNOs) worldwide are extensively deploying LTE networks in different spectrum bands and utilising different bandwidth configurations. Initially, the deployment is coverage oriented with macro cells using the lower LTE spectrum bands. As the offered traffic (i.e. the requested traffic from the users) increases the LTE deployment evolves with macro cells expanded with additional capacity boosting LTE carriers in higher frequency bands complemented with micro or small cells in traffic hotspot areas. For MNOs it is crucial to use the LTE spectrum assets, as well as the installed network infrastructure, in the most cost efficient way. The dynamic spectrum allocation (DSA) aims at (de)activating the available LTE frequency carriers according to the temporal and spatial traffic variations in order to increase the overall LTE system performance in terms of total network capacity by reducing the interference. This paper evaluates the DSA potential of achieving the envisaged performance improvement and identifying in which system and traffic conditions the DSA should be deployed. A self-optimised network (SON) DSA algorithm is also proposed and evaluated. The evaluations have been carried out in a hexagonal and a realistic site-specific urban macro layout assuming a central traffic hotspot area surrounded with an area of lower traffic with a total size of approximately 8 × 8 km2. The results show that up to 47 % and up to 40 % possible DSA gains are achievable with regards to the carried system load (i.e. used resources) for homogenous traffic distribution with hexagonal layout and for realistic site-specific urban macro layout, respectively. The SON DSA algorithm evaluation in a realistic site-specific urban macro cell deployment scenario including realistic non-uniform spatial traffic distribution shows insignificant cell throughput (i.e. served traffic) performance gains. Nevertheless, in the SON DSA investigations, a gain of up to 25 % has been observed when analysing the resource utilisation in the non-hotspot cells.

Performance evaluation of multi-stratum resources integration based on network function virtualization in software defined elastic data center optical interconnect.

PubMed

Yang, Hui; Zhang, Jie; Ji, Yuefeng; Tian, Rui; Han, Jianrui; Lee, Young

2015-11-30

Data center interconnect with elastic optical network is a promising scenario to meet the high burstiness and high-bandwidth requirements of data center services. In our previous work, we implemented multi-stratum resilience between IP and elastic optical networks that allows to accommodate data center services. In view of this, this study extends to consider the resource integration by breaking the limit of network device, which can enhance the resource utilization. We propose a novel multi-stratum resources integration (MSRI) architecture based on network function virtualization in software defined elastic data center optical interconnect. A resource integrated mapping (RIM) scheme for MSRI is introduced in the proposed architecture. The MSRI can accommodate the data center services with resources integration when the single function or resource is relatively scarce to provision the services, and enhance globally integrated optimization of optical network and application resources. The overall feasibility and efficiency of the proposed architecture are experimentally verified on the control plane of OpenFlow-based enhanced software defined networking (eSDN) testbed. The performance of RIM scheme under heavy traffic load scenario is also quantitatively evaluated based on MSRI architecture in terms of path blocking probability, provisioning latency and resource utilization, compared with other provisioning schemes.

Network capability estimation. Vela network evaluation and automatic processing research. Technical report. [NETWORTH

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

Snell, N.S.

1976-09-24

NETWORTH is a computer program which calculates the detection and location capability of seismic networks. A modified version of NETWORTH has been developed. This program has been used to evaluate the effect of station 'downtime', the signal amplitude variance, and the station detection threshold upon network detection capability. In this version all parameters may be changed separately for individual stations. The capability of using signal amplitude corrections has been added. The function of amplitude corrections is to remove possible bias in the magnitude estimate due to inhomogeneous signal attenuation. These corrections may be applied to individual stations, individual epicenters, ormore » individual station/epicenter combinations. An option has been added to calculate the effect of station 'downtime' upon network capability. This study indicates that, if capability loss due to detection errors can be minimized, then station detection threshold and station reliability will be the fundamental limits to network performance. A baseline network of thirteen stations has been performed. These stations are as follows: Alaskan Long Period Array, (ALPA); Ankara, (ANK); Chiang Mai, (CHG); Korean Seismic Research Station, (KSRS); Large Aperture Seismic Array, (LASA); Mashhad, (MSH); Mundaring, (MUN); Norwegian Seismic Array, (NORSAR); New Delhi, (NWDEL); Red Knife, Ontario, (RK-ON); Shillong, (SHL); Taipei, (TAP); and White Horse, Yukon, (WH-YK).« less

Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting

PubMed Central

Ghazali, Rozaida; Herawan, Tutut

2016-01-01

Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network. PMID:27959927

Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting.

PubMed

Waheeb, Waddah; Ghazali, Rozaida; Herawan, Tutut

2016-01-01

Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.

Total MRI Small Vessel Disease Burden Correlates with Cognitive Performance, Cortical Atrophy, and Network Measures in a Memory Clinic Population.

PubMed

Banerjee, Gargi; Jang, Hyemin; Kim, Hee Jin; Kim, Sung Tae; Kim, Jae Seung; Lee, Jae Hong; Im, Kiho; Kwon, Hunki; Lee, Jong Min; Na, Duk L; Seo, Sang Won; Werring, David John

2018-01-01

Recent evidence suggests that combining individual imaging markers of cerebral small vessel disease (SVD) may more accurately reflect its overall burden and better correlate with clinical measures. We wished to establish the clinical relevance of the total SVD score in a memory clinic population by investigating the association with SVD score and cognitive performance, cortical atrophy, and structural network measures, after adjusting for amyloid-β burden. We included 243 patients with amnestic mild cognitive impairment (MCI), Alzheimer's disease dementia, subcortical vascular MCI, or subcortical vascular dementia. All underwent MR and [11C] PiB-PET scanning and had standardized cognitive testing. Multiple linear regression was used to evaluate the relationships between SVD score and cognition, cortical thickness, and structural network measures. Path analyses were performed to evaluate whether network disruption mediates the effects of SVD score on cortical thickness and cognition. Total SVD score was associated with the performance of frontal (β - 4.31, SE 2.09, p = 0.040) and visuospatial (β - 0.95, SE 0.44, p = 0.032) tasks, and with reduced cortical thickness in widespread brain regions. Total SVD score was negatively correlated with nodal efficiency, as well as changes in brain network organization, with evidence of reduced integration and increasing segregation. Path analyses showed that the associations between SVD score and frontal and visuospatial scores were partially mediated by decreases in their corresponding nodal efficiency and cortical thickness. Total SVD burden has clinical relevance in a memory clinic population and correlates with cognition, and cortical atrophy, as well as structural network disruption.

A review on transport layer protocol performance for delivering video on an adhoc network

NASA Astrophysics Data System (ADS)

Suherman; Suwendri; Al-Akaidi, Marwan

2017-09-01

The transport layer protocol is responsible for the end to end data transmission. Transmission control protocol (TCP) provides a reliable connection and user datagram protocol (UDP) offers fast but unguaranteed data transfer. Meanwhile, the 802.11 (wireless fidelity/WiFi) networks have been widely used as internet hotspots. This paper evaluates TCP, TCP variants and UDP performances for video transmission on an adhoc network. The transport protocol - medium access cross-layer is proposed by prioritizing TCP acknowledgement to reduce delay. The NS-2 evaluations show that the average delays increase linearly for all the evaluated protocols and the average packet losses grow logarithmically. UDP produces the lowest transmission delay; 5.4% and 5.8% lower than TCP and TCP variant, but experiences the highest packet loss. Both TCP and TCP Vegas maintain packet loss as low as possible. The proposed cross-layer successfully decreases TCP and TCP Vegas delay about 0.12 % and 0.15%, although losses remain similar.

Statistical porcess control in Deep Space Network operation

NASA Technical Reports Server (NTRS)

Hodder, J. A.

2002-01-01

This report describes how the Deep Space Mission System (DSMS) Operations Program Office at the Jet Propulsion Laboratory's (EL) uses Statistical Process Control (SPC) to monitor performance and evaluate initiatives for improving processes on the National Aeronautics and Space Administration's (NASA) Deep Space Network (DSN).

A Proxy Design to Leverage the Interconnection of CoAP Wireless Sensor Networks with Web Applications

PubMed Central

Ludovici, Alessandro; Calveras, Anna

2015-01-01

In this paper, we present the design of a Constrained Application Protocol (CoAP) proxy able to interconnect Web applications based on Hypertext Transfer Protocol (HTTP) and WebSocket with CoAP based Wireless Sensor Networks. Sensor networks are commonly used to monitor and control physical objects or environments. Smart Cities represent applications of such a nature. Wireless Sensor Networks gather data from their surroundings and send them to a remote application. This data flow may be short or long lived. The traditional HTTP long-polling used by Web applications may not be adequate in long-term communications. To overcome this problem, we include the WebSocket protocol in the design of the CoAP proxy. We evaluate the performance of the CoAP proxy in terms of latency and memory consumption. The tests consider long and short-lived communications. In both cases, we evaluate the performance obtained by the CoAP proxy according to the use of WebSocket and HTTP long-polling. PMID:25585107

A generalized network flow model for the multi-mode resource-constrained project scheduling problem with discounted cash flows

NASA Astrophysics Data System (ADS)

Chen, Miawjane; Yan, Shangyao; Wang, Sin-Siang; Liu, Chiu-Lan

2015-02-01

An effective project schedule is essential for enterprises to increase their efficiency of project execution, to maximize profit, and to minimize wastage of resources. Heuristic algorithms have been developed to efficiently solve the complicated multi-mode resource-constrained project scheduling problem with discounted cash flows (MRCPSPDCF) that characterize real problems. However, the solutions obtained in past studies have been approximate and are difficult to evaluate in terms of optimality. In this study, a generalized network flow model, embedded in a time-precedence network, is proposed to formulate the MRCPSPDCF with the payment at activity completion times. Mathematically, the model is formulated as an integer network flow problem with side constraints, which can be efficiently solved for optimality, using existing mathematical programming software. To evaluate the model performance, numerical tests are performed. The test results indicate that the model could be a useful planning tool for project scheduling in the real world.

High fidelity wireless network evaluation for heterogeneous cognitive radio networks

NASA Astrophysics Data System (ADS)

Ding, Lei; Sagduyu, Yalin; Yackoski, Justin; Azimi-Sadjadi, Babak; Li, Jason; Levy, Renato; Melodia, Tammaso

2012-06-01

We present a high fidelity cognitive radio (CR) network emulation platform for wireless system tests, measure- ments, and validation. This versatile platform provides the configurable functionalities to control and repeat realistic physical channel effects in integrated space, air, and ground networks. We combine the advantages of scalable simulation environment with reliable hardware performance for high fidelity and repeatable evaluation of heterogeneous CR networks. This approach extends CR design only at device (software-defined-radio) or lower-level protocol (dynamic spectrum access) level to end-to-end cognitive networking, and facilitates low-cost deployment, development, and experimentation of new wireless network protocols and applications on frequency- agile programmable radios. Going beyond the channel emulator paradigm for point-to-point communications, we can support simultaneous transmissions by network-level emulation that allows realistic physical-layer inter- actions between diverse user classes, including secondary users, primary users, and adversarial jammers in CR networks. In particular, we can replay field tests in a lab environment with real radios perceiving and learning the dynamic environment thereby adapting for end-to-end goals over distributed spectrum coordination channels that replace the common control channel as a single point of failure. CR networks offer several dimensions of tunable actions including channel, power, rate, and route selection. The proposed network evaluation platform is fully programmable and can reliably evaluate the necessary cross-layer design solutions with configurable op- timization space by leveraging the hardware experiments to represent the realistic effects of physical channel, topology, mobility, and jamming on spectrum agility, situational awareness, and network resiliency. We also provide the flexibility to scale up the test environment by introducing virtual radios and establishing seamless signal-level interactions with real radios. This holistic wireless evaluation approach supports a large-scale, het- erogeneous, and dynamic CR network architecture and allows developing cross-layer network protocols under high fidelity, repeatable, and scalable wireless test scenarios suitable for heterogeneous space, air, and ground networks.

A Comparative Study of Unsupervised Anomaly Detection Techniques Using Honeypot Data

NASA Astrophysics Data System (ADS)

Song, Jungsuk; Takakura, Hiroki; Okabe, Yasuo; Inoue, Daisuke; Eto, Masashi; Nakao, Koji

Intrusion Detection Systems (IDS) have been received considerable attention among the network security researchers as one of the most promising countermeasures to defend our crucial computer systems or networks against attackers on the Internet. Over the past few years, many machine learning techniques have been applied to IDSs so as to improve their performance and to construct them with low cost and effort. Especially, unsupervised anomaly detection techniques have a significant advantage in their capability to identify unforeseen attacks, i.e., 0-day attacks, and to build intrusion detection models without any labeled (i.e., pre-classified) training data in an automated manner. In this paper, we conduct a set of experiments to evaluate and analyze performance of the major unsupervised anomaly detection techniques using real traffic data which are obtained at our honeypots deployed inside and outside of the campus network of Kyoto University, and using various evaluation criteria, i.e., performance evaluation by similarity measurements and the size of training data, overall performance, detection ability for unknown attacks, and time complexity. Our experimental results give some practical and useful guidelines to IDS researchers and operators, so that they can acquire insight to apply these techniques to the area of intrusion detection, and devise more effective intrusion detection models.

On-the-field performance of quintuple-play long-reach OFDM-based WDM-PON optical access networks.

PubMed

Llorente, Roberto; Morant, Maria; Pellicer, Eloy; Herman, Milan; Nagy, Zsolt; Alves, Tiago; Cartaxo, Adolfo; Herrera, Javier; Correcher, Jose; Quinlan, Terence; Walker, Stuart; Rodrigues, Cláudio; Cluzeaud, Pierre; Schmidt, Axel; Piesiewicz, Radoslaw; Sambaraju, Rakesh

2014-03-24

In this paper the on-the-field performance of a WDM-PON optical access providing quintuple-play services using orthogonal frequency division multiplexing (OFDM) modulation is evaluated in a real fiber-to-the-home (FTTH) network deployed by Towercom operator in Bratislava (Slovakia). A bundle of quintuple-play services comprising full-standard OFDM-based signals (LTE, WiMAX, UWB and DVB-T) and an ad-hoc OFDM-GbE signal is transmitted in coexistence per single user. Both downstream and upstream transmission performances are evaluated in different on-the-field long-reach optical link distance configurations. Four wavelength multi-user transmission of quintuple-play OFDM services is demonstrated exceeding 60.8 km reach in standard single mode fiber.

Computation and Communication Evaluation of an Authentication Mechanism for Time-Triggered Networked Control Systems

PubMed Central

Martins, Goncalo; Moondra, Arul; Dubey, Abhishek; Bhattacharjee, Anirban; Koutsoukos, Xenofon D.

2016-01-01

In modern networked control applications, confidentiality and integrity are important features to address in order to prevent against attacks. Moreover, network control systems are a fundamental part of the communication components of current cyber-physical systems (e.g., automotive communications). Many networked control systems employ Time-Triggered (TT) architectures that provide mechanisms enabling the exchange of precise and synchronous messages. TT systems have computation and communication constraints, and with the aim to enable secure communications in the network, it is important to evaluate the computational and communication overhead of implementing secure communication mechanisms. This paper presents a comprehensive analysis and evaluation of the effects of adding a Hash-based Message Authentication (HMAC) to TT networked control systems. The contributions of the paper include (1) the analysis and experimental validation of the communication overhead, as well as a scalability analysis that utilizes the experimental result for both wired and wireless platforms and (2) an experimental evaluation of the computational overhead of HMAC based on a kernel-level Linux implementation. An automotive application is used as an example, and the results show that it is feasible to implement a secure communication mechanism without interfering with the existing automotive controller execution times. The methods and results of the paper can be used for evaluating the performance impact of security mechanisms and, thus, for the design of secure wired and wireless TT networked control systems. PMID:27463718

Computation and Communication Evaluation of an Authentication Mechanism for Time-Triggered Networked Control Systems.

PubMed

Martins, Goncalo; Moondra, Arul; Dubey, Abhishek; Bhattacharjee, Anirban; Koutsoukos, Xenofon D

2016-07-25

In modern networked control applications, confidentiality and integrity are important features to address in order to prevent against attacks. Moreover, network control systems are a fundamental part of the communication components of current cyber-physical systems (e.g., automotive communications). Many networked control systems employ Time-Triggered (TT) architectures that provide mechanisms enabling the exchange of precise and synchronous messages. TT systems have computation and communication constraints, and with the aim to enable secure communications in the network, it is important to evaluate the computational and communication overhead of implementing secure communication mechanisms. This paper presents a comprehensive analysis and evaluation of the effects of adding a Hash-based Message Authentication (HMAC) to TT networked control systems. The contributions of the paper include (1) the analysis and experimental validation of the communication overhead, as well as a scalability analysis that utilizes the experimental result for both wired and wireless platforms and (2) an experimental evaluation of the computational overhead of HMAC based on a kernel-level Linux implementation. An automotive application is used as an example, and the results show that it is feasible to implement a secure communication mechanism without interfering with the existing automotive controller execution times. The methods and results of the paper can be used for evaluating the performance impact of security mechanisms and, thus, for the design of secure wired and wireless TT networked control systems.

Performance Evaluation of FAST TCP Traffic-Flows in Multihomed MANETs

NASA Astrophysics Data System (ADS)

Mudassir, Mumajjed Ul; Akram, Adeel

In Mobile Ad hoc Networks (MANETs) an efficient communication protocol is required at the transport layer. Mobile nodes moving around will have temporary and rather short-lived connectivity with each other and the Internet, thus requiring efficient utilization of network resources. Moreover the problems arising due to high mobility, collision and congestion must also be considered. Multihoming allows higher reliability and enhancement of network throughput. FAST TCP is a new promising transport layer protocol developed for high-speed high-latency networks. In this paper, we have analyzed the performance of FAST TCP traffic flows in multihomed MANETs and compared it with standard TCP (TCP Reno) traffic flows in non-multihomed MANETs.

Team assembly mechanisms determine collaboration network structure and team performance.

PubMed

Guimerà, Roger; Uzzi, Brian; Spiro, Jarrett; Amaral, Luís A Nunes

2005-04-29

Agents in creative enterprises are embedded in networks that inspire, support, and evaluate their work. Here, we investigate how the mechanisms by which creative teams self-assemble determine the structure of these collaboration networks. We propose a model for the self-assembly of creative teams that has its basis in three parameters: team size, the fraction of newcomers in new productions, and the tendency of incumbents to repeat previous collaborations. The model suggests that the emergence of a large connected community of practitioners can be described as a phase transition. We find that team assembly mechanisms determine both the structure of the collaboration network and team performance for teams derived from both artistic and scientific fields.

Interconnection network architectures based on integrated orbital angular momentum emitters

NASA Astrophysics Data System (ADS)

Scaffardi, Mirco; Zhang, Ning; Malik, Muhammad Nouman; Lazzeri, Emma; Klitis, Charalambos; Lavery, Martin; Sorel, Marc; Bogoni, Antonella

2018-02-01

Novel architectures for two-layer interconnection networks based on concentric OAM emitters are presented. A scalability analysis is done in terms of devices characteristics, power budget and optical signal to noise ratio by exploiting experimentally measured parameters. The analysis shows that by exploiting optical amplifications, the proposed interconnection networks can support a number of ports higher than 100. The OAM crosstalk induced-penalty, evaluated through an experimental characterization, do not significantly affect the interconnection network performance.

An iteration algorithm for optimal network flows

NASA Astrophysics Data System (ADS)

Woong, C. J.

1983-09-01

A packet switching network has the desirable feature of rapidly handling short (bursty) messages of the type often found in computer communication systems. In evaluating packet switching networks, the average time delay per packet is one of the most important measures of performance. The problem of message routing to minimize time delay is analyzed here using two approaches, called "successive saturation' and "max-slack', for various traffic requirement matrices and networks with fixed topology and link capacities.

Remote-seeded WDM-PON upgrade using linear semiconductor opticalamplifiers

NASA Astrophysics Data System (ADS)

Martínez, J. J.; Merayo, N.; Villafranca, A.; Garcés, I.

2013-05-01

In this work we have assessed the capacity of a linear (gain-clamped) semiconductor optical amplifier to enhance the budget of WDM PON network links for their evolution from FTTC to FTTH access. A wavelength-seeded network architecture has been considered, evaluating the performance improvement obtained by the use of an amplifier for the cases of link reach extension and optical splitting to reach end users. The evaluation measurements have shown that the extra budget is enough to compensate for the losses of a passive splitter up to atleast 1:16 division rate or to highly increment reach of the network.

Integration of FMIPv6 in HMIPv6 to Improve Hand-over Performance

NASA Astrophysics Data System (ADS)

Patil, Dipali P.; Patil, G. A.

2010-11-01

Mobile users move frequently between networks, as they stay connected to the Internet. Thus, as mobility increases across networks, handovers will significantly impact the quality of the connection and user application. Handover performance is very important when evaluating IP mobility protocols. Since handover request are driven by several needs such as cost reduction criteria, network resource optimization and service related requirements. Current works to support seamless mobility in IPv6 network are classified into HMIPv6 and FMIPv6. These two approaches have pros and cons respectively and are being standardized independently in IETF. If one can integrate properly these two approaches, it is expected that the one can get more effective protocols that can provide better handover performance. This paper integrates FHMIPv6 in HMIPv6 (F-HMIPv6) so as to provide effectively fast handover on the hierarchical Mobile IPv6. The simulation performed using Ns-2 extensions to show that a performance of proposed system is better in terms of packet loss and hand-over delay.

The Researching on Evaluation of Automatic Voltage Control Based on Improved Zoning Methodology

NASA Astrophysics Data System (ADS)

Xiao-jun, ZHU; Ang, FU; Guang-de, DONG; Rui-miao, WANG; De-fen, ZHU

2018-03-01

According to the present serious phenomenon of increasing size and structure of power system, hierarchically structured automatic voltage control(AVC) has been the researching spot. In the paper, the reduced control model is built and the adaptive reduced control model is researched to improve the voltage control effect. The theories of HCSD, HCVS, SKC and FCM are introduced and the effect on coordinated voltage regulation caused by different zoning methodologies is also researched. The generic framework for evaluating performance of coordinated voltage regulation is built. Finally, the IEEE-96 stsyem is used to divide the network. The 2383-bus Polish system is built to verify that the selection of a zoning methodology affects not only the coordinated voltage regulation operation, but also its robustness to erroneous data and proposes a comprehensive generic framework for evaluating its performance. The New England 39-bus network is used to verify the adaptive reduced control models’ performance.

Neural Network-Based Sensor Validation for Turboshaft Engines

NASA Technical Reports Server (NTRS)

Moller, James C.; Litt, Jonathan S.; Guo, Ten-Huei

1998-01-01

Sensor failure detection, isolation, and accommodation using a neural network approach is described. An auto-associative neural network is configured to perform dimensionality reduction on the sensor measurement vector and provide estimated sensor values. The sensor validation scheme is applied in a simulation of the T700 turboshaft engine in closed loop operation. Performance is evaluated based on the ability to detect faults correctly and maintain stable and responsive engine operation. The set of sensor outputs used for engine control forms the network input vector. Analytical redundancy is verified by training networks of successively smaller bottleneck layer sizes. Training data generation and strategy are discussed. The engine maintained stable behavior in the presence of sensor hard failures. With proper selection of fault determination thresholds, stability was maintained in the presence of sensor soft failures.

Landscape genetics for the empirical assessment of resistance surfaces: The European pine marten (Martes martes) as a target-species of a regional ecological network

Treesearch

Aritz Ruiz-Gonzalez; Mikel Gurrutxaga; Samuel A. Cushman; Maria Jose Madeira; Ettore Randi; Benjamin J. Gomez-Moliner

2014-01-01

Coherent ecological networks (EN) composed of core areas linked by ecological corridors are being developed worldwide with the goal of promoting landscape connectivity and biodiversity conservation. However, empirical assessment of the performance of EN designs is critical to evaluate the utility of these networks to mitigate effects of habitat loss and...

Evaluating the Limits of Network Topology Inference Via Virtualized Network Emulation

DTIC Science & Technology

2015-06-01

76 xi Figure 5.33 Hop-plot of five best reduction methods. KDD most closely matches the Internet plot...respectively, located around the world. These monitors provide locations from which to perform network measurement experiments, primarily using the ping ...International Symposium on Modeling, Analysis and Simulation of Computer Telecommunication Systems. IEEE, 2001, pp. 346–353. 90 [21] C. Jin , Q. Chen, and S

Evaluation of artificial time series microarray data for dynamic gene regulatory network inference.

PubMed

Xenitidis, P; Seimenis, I; Kakolyris, S; Adamopoulos, A

2017-08-07

High-throughput technology like microarrays is widely used in the inference of gene regulatory networks (GRNs). We focused on time series data since we are interested in the dynamics of GRNs and the identification of dynamic networks. We evaluated the amount of information that exists in artificial time series microarray data and the ability of an inference process to produce accurate models based on them. We used dynamic artificial gene regulatory networks in order to create artificial microarray data. Key features that characterize microarray data such as the time separation of directly triggered genes, the percentage of directly triggered genes and the triggering function type were altered in order to reveal the limits that are imposed by the nature of microarray data on the inference process. We examined the effect of various factors on the inference performance such as the network size, the presence of noise in microarray data, and the network sparseness. We used a system theory approach and examined the relationship between the pole placement of the inferred system and the inference performance. We examined the relationship between the inference performance in the time domain and the true system parameter identification. Simulation results indicated that time separation and the percentage of directly triggered genes are crucial factors. Also, network sparseness, the triggering function type and noise in input data affect the inference performance. When two factors were simultaneously varied, it was found that variation of one parameter significantly affects the dynamic response of the other. Crucial factors were also examined using a real GRN and acquired results confirmed simulation findings with artificial data. Different initial conditions were also used as an alternative triggering approach. Relevant results confirmed that the number of datasets constitutes the most significant parameter with regard to the inference performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

The simulation of cropping pattern to improve the performance of irrigation network in Cau irrigation area

NASA Astrophysics Data System (ADS)

Wahyuningsih, Retno; Rintis Hadiani, RR; Sobriyah

2017-01-01

Cau irrigation area located in Madiun district, East Java Province, irrigates 1.232 Ha of land which covers Cau primary channel irrigation network, Wungu Secondary channel irrigation network, and Grape secondary channel irrigation network. The problems in Cau irrigation area are limited availability of water especially during the dry season (planting season II and III) and non-compliance to cropping patterns. The evaluation of irrigation system performance of Cau irrigation area needs to be done in order to know how far the irrigation system performance is, especially based on planting productivity aspect. The improvement of irrigation network performance through cropping pattern optimization is based on the increase of water necessity fulfillment (k factor), the realization of planting area and rice productivity. The research method of irrigation system performance is by analyzing the secondary data based on the Regulation of Ministry of Public Work and State Minister for Public Housing Number: 12/PRT/M/2015. The analysis of water necessity fulfillment (k factor) uses Public Work Plan Criteria Method. The performance level of planting productivity aspect in existing condition is 87.10%, alternative 1 is 93.90% dan alternative 2 is 96.90%. It means that the performance of the irrigation network from productivity aspect increases 6.80% for alternative 1 and 9.80% for alternative 2.

Back Propagation Neural Network Model for Predicting the Performance of Immobilized Cell Biofilters Handling Gas-Phase Hydrogen Sulphide and Ammonia

PubMed Central

Rene, Eldon R.; López, M. Estefanía; Kim, Jung Hoon; Park, Hung Suck

2013-01-01

Lab scale studies were conducted to evaluate the performance of two simultaneously operated immobilized cell biofilters (ICBs) for removing hydrogen sulphide (H2S) and ammonia (NH3) from gas phase. The removal efficiencies (REs) of the biofilter treating H2S varied from 50 to 100% at inlet loading rates (ILRs) varying up to 13 g H2S/m3 ·h, while the NH3 biofilter showed REs ranging from 60 to 100% at ILRs varying between 0.5 and 5.5 g NH3/m3 ·h. An application of the back propagation neural network (BPNN) to predict the performance parameter, namely, RE (%) using this experimental data is presented in this paper. The input parameters to the network were unit flow (per min) and inlet concentrations (ppmv), respectively. The accuracy of BPNN-based model predictions were evaluated by providing the trained network topology with a test dataset and also by calculating the regression coefficient (R 2) values. The results from this predictive modeling work showed that BPNNs were able to predict the RE of both the ICBs efficiently. PMID:24307999

Network speech systems technology program

NASA Astrophysics Data System (ADS)

Weinstein, C. J.

1981-09-01

This report documents work performed during FY 1981 on the DCA-sponsored Network Speech Systems Technology Program. The two areas of work reported are: (1) communication system studies in support of the evolving Defense Switched Network (DSN) and (2) design and implementation of satellite/terrestrial interfaces for the Experimental Integrated Switched Network (EISN). The system studies focus on the development and evaluation of economical and endurable network routing procedures. Satellite/terrestrial interface development includes circuit-switched and packet-switched connections to the experimental wideband satellite network. Efforts in planning and coordination of EISN experiments are reported in detail in a separate EISN Experiment Plan.

Research on the Establishment and Evaluation of End - to - End Service Quality Index System

NASA Astrophysics Data System (ADS)

Wei, Chen; Jing, Tao; Ji, Yutong

2018-01-01

From the perspective of power data networks, put forward the index system model to measure the quality of service, covering user experience, business performance, network capacity support, etc., and gives the establishment and use of each layer index in the model.

Correcting evaluation bias of relational classifiers with network cross validation

DOE PAGES

Neville, Jennifer; Gallagher, Brian; Eliassi-Rad, Tina; ...

2011-01-04

Recently, a number of modeling techniques have been developed for data mining and machine learning in relational and network domains where the instances are not independent and identically distributed (i.i.d.). These methods specifically exploit the statistical dependencies among instances in order to improve classification accuracy. However, there has been little focus on how these same dependencies affect our ability to draw accurate conclusions about the performance of the models. More specifically, the complex link structure and attribute dependencies in relational data violate the assumptions of many conventional statistical tests and make it difficult to use these tests to assess themore » models in an unbiased manner. In this work, we examine the task of within-network classification and the question of whether two algorithms will learn models that will result in significantly different levels of performance. We show that the commonly used form of evaluation (paired t-test on overlapping network samples) can result in an unacceptable level of Type I error. Furthermore, we show that Type I error increases as (1) the correlation among instances increases and (2) the size of the evaluation set increases (i.e., the proportion of labeled nodes in the network decreases). Lastly, we propose a method for network cross-validation that combined with paired t-tests produces more acceptable levels of Type I error while still providing reasonable levels of statistical power (i.e., 1–Type II error).« less

Evaluation of a 433 MHz band body sensor network for biomedical applications.

PubMed

Kim, Saim; Brendle, Christian; Lee, Hyun-Young; Walter, Marian; Gloeggler, Sigrid; Krueger, Stefan; Leonhardt, Steffen

2013-01-14

Body sensor networks (BSN) are an important research topic due to various advantages over conventional measurement equipment. One main advantage is the feasibility to deploy a BSN system for 24/7 health monitoring applications. The requirements for such an application are miniaturization of the network nodes and the use of wireless data transmission technologies to ensure wearability and ease of use. Therefore, the reliability of such a system depends on the quality of the wireless data transmission. At present, most BSNs use ZigBee or other IEEE 802.15.4 based transmission technologies. Here, we evaluated the performance of a wireless transmission system of a novel BSN for biomedical applications in the 433MHz ISM band, called Integrated Posture and Activity NEtwork by Medit Aachen (IPANEMA) BSN. The 433MHz ISM band is used mostly by implanted sensors and thus allows easy integration of such into the BSN. Multiple measurement scenarios have been assessed, including varying antenna orientations, transmission distances and the number of network participants. The mean packet loss rate (PLR) was 0.63% for a single slave, which is comparable to IEEE 802.15.4 BSNs in the proximity of Bluetooth or WiFi networks. Secondly, an enhanced version is evaluated during on-body measurements with five slaves. The mean PLR results show a comparable good performance for measurements on a treadmill (2.5%), an outdoor track (3.4%) and in a climate chamber (1.5%).

A reverse engineering approach to optimize experiments for the construction of biological regulatory networks.

PubMed

Zhang, Xiaomeng; Shao, Bin; Wu, Yangle; Qi, Ouyang

2013-01-01

One of the major objectives in systems biology is to understand the relation between the topological structures and the dynamics of biological regulatory networks. In this context, various mathematical tools have been developed to deduct structures of regulatory networks from microarray expression data. In general, from a single data set, one cannot deduct the whole network structure; additional expression data are usually needed. Thus how to design a microarray expression experiment in order to get the most information is a practical problem in systems biology. Here we propose three methods, namely, maximum distance method, trajectory entropy method, and sampling method, to derive the optimal initial conditions for experiments. The performance of these methods is tested and evaluated in three well-known regulatory networks (budding yeast cell cycle, fission yeast cell cycle, and E. coli. SOS network). Based on the evaluation, we propose an efficient strategy for the design of microarray expression experiments.

Stochastic availability analysis of operational data systems in the Deep Space Network

NASA Technical Reports Server (NTRS)

Issa, T. N.

1991-01-01

Existing availability models of standby redundant systems consider only an operator's performance and its interaction with the hardware performance. In the case of operational data systems in the Deep Space Network (DSN), in addition to an operator system interface, a controller reconfigures the system and links a standby unit into the network data path upon failure of the operating unit. A stochastic (Markovian) process technique is used to model and analyze the availability performance and occurrence of degradation due to partial failures are quantitatively incorporated into the model. Exact expressions of the steady state availability and proportion degraded performance measures are derived for the systems under study. The interaction among the hardware, operator, and controller performance parameters and that interaction's effect on data availability are evaluated and illustrated for an operational data processing system.

Complex Networks Analysis of Manual and Machine Translations

NASA Astrophysics Data System (ADS)

Amancio, Diego R.; Antiqueira, Lucas; Pardo, Thiago A. S.; da F. Costa, Luciano; Oliveira, Osvaldo N.; Nunes, Maria G. V.

Complex networks have been increasingly used in text analysis, including in connection with natural language processing tools, as important text features appear to be captured by the topology and dynamics of the networks. Following previous works that apply complex networks concepts to text quality measurement, summary evaluation, and author characterization, we now focus on machine translation (MT). In this paper we assess the possible representation of texts as complex networks to evaluate cross-linguistic issues inherent in manual and machine translation. We show that different quality translations generated by MT tools can be distinguished from their manual counterparts by means of metrics such as in- (ID) and out-degrees (OD), clustering coefficient (CC), and shortest paths (SP). For instance, we demonstrate that the average OD in networks of automatic translations consistently exceeds the values obtained for manual ones, and that the CC values of source texts are not preserved for manual translations, but are for good automatic translations. This probably reflects the text rearrangements humans perform during manual translation. We envisage that such findings could lead to better MT tools and automatic evaluation metrics.

Local Difference Measures between Complex Networks for Dynamical System Model Evaluation

PubMed Central

Lange, Stefan; Donges, Jonathan F.; Volkholz, Jan; Kurths, Jürgen

2015-01-01

A faithful modeling of real-world dynamical systems necessitates model evaluation. A recent promising methodological approach to this problem has been based on complex networks, which in turn have proven useful for the characterization of dynamical systems. In this context, we introduce three local network difference measures and demonstrate their capabilities in the field of climate modeling, where these measures facilitate a spatially explicit model evaluation. Building on a recent study by Feldhoff et al. [1] we comparatively analyze statistical and dynamical regional climate simulations of the South American monsoon system. Three types of climate networks representing different aspects of rainfall dynamics are constructed from the modeled precipitation space-time series. Specifically, we define simple graphs based on positive as well as negative rank correlations between rainfall anomaly time series at different locations, and such based on spatial synchronizations of extreme rain events. An evaluation against respective networks built from daily satellite data provided by the Tropical Rainfall Measuring Mission 3B42 V7 reveals far greater differences in model performance between network types for a fixed but arbitrary climate model than between climate models for a fixed but arbitrary network type. We identify two sources of uncertainty in this respect. Firstly, climate variability limits fidelity, particularly in the case of the extreme event network; and secondly, larger geographical link lengths render link misplacements more likely, most notably in the case of the anticorrelation network; both contributions are quantified using suitable ensembles of surrogate networks. Our model evaluation approach is applicable to any multidimensional dynamical system and especially our simple graph difference measures are highly versatile as the graphs to be compared may be constructed in whatever way required. Generalizations to directed as well as edge- and node-weighted graphs are discussed. PMID:25856374

Local difference measures between complex networks for dynamical system model evaluation.

PubMed

Lange, Stefan; Donges, Jonathan F; Volkholz, Jan; Kurths, Jürgen

2015-01-01

A faithful modeling of real-world dynamical systems necessitates model evaluation. A recent promising methodological approach to this problem has been based on complex networks, which in turn have proven useful for the characterization of dynamical systems. In this context, we introduce three local network difference measures and demonstrate their capabilities in the field of climate modeling, where these measures facilitate a spatially explicit model evaluation.Building on a recent study by Feldhoff et al. [8] we comparatively analyze statistical and dynamical regional climate simulations of the South American monsoon system [corrected]. types of climate networks representing different aspects of rainfall dynamics are constructed from the modeled precipitation space-time series. Specifically, we define simple graphs based on positive as well as negative rank correlations between rainfall anomaly time series at different locations, and such based on spatial synchronizations of extreme rain events. An evaluation against respective networks built from daily satellite data provided by the Tropical Rainfall Measuring Mission 3B42 V7 reveals far greater differences in model performance between network types for a fixed but arbitrary climate model than between climate models for a fixed but arbitrary network type. We identify two sources of uncertainty in this respect. Firstly, climate variability limits fidelity, particularly in the case of the extreme event network; and secondly, larger geographical link lengths render link misplacements more likely, most notably in the case of the anticorrelation network; both contributions are quantified using suitable ensembles of surrogate networks. Our model evaluation approach is applicable to any multidimensional dynamical system and especially our simple graph difference measures are highly versatile as the graphs to be compared may be constructed in whatever way required. Generalizations to directed as well as edge- and node-weighted graphs are discussed.

Compressive sensing of high betweenness centrality nodes in networks

NASA Astrophysics Data System (ADS)

Mahyar, Hamidreza; Hasheminezhad, Rouzbeh; Ghalebi K., Elahe; Nazemian, Ali; Grosu, Radu; Movaghar, Ali; Rabiee, Hamid R.

2018-05-01

Betweenness centrality is a prominent centrality measure expressing importance of a node within a network, in terms of the fraction of shortest paths passing through that node. Nodes with high betweenness centrality have significant impacts on the spread of influence and idea in social networks, the user activity in mobile phone networks, the contagion process in biological networks, and the bottlenecks in communication networks. Thus, identifying k-highest betweenness centrality nodes in networks will be of great interest in many applications. In this paper, we introduce CS-HiBet, a new method to efficiently detect top- k betweenness centrality nodes in networks, using compressive sensing. CS-HiBet can perform as a distributed algorithm by using only the local information at each node. Hence, it is applicable to large real-world and unknown networks in which the global approaches are usually unrealizable. The performance of the proposed method is evaluated by extensive simulations on several synthetic and real-world networks. The experimental results demonstrate that CS-HiBet outperforms the best existing methods with notable improvements.

Design of Neural Networks for Fast Convergence and Accuracy

NASA Technical Reports Server (NTRS)

Maghami, Peiman G.; Sparks, Dean W., Jr.

1998-01-01

A novel procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed to provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component spacecraft design changes and measures of its performance. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The design algorithm attempts to avoid the local minima phenomenon that hampers the traditional network training. A numerical example is performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

Subjective evaluation of H.265/HEVC based dynamic adaptive video streaming over HTTP (HEVC-DASH)

NASA Astrophysics Data System (ADS)

Irondi, Iheanyi; Wang, Qi; Grecos, Christos

2015-02-01

The Dynamic Adaptive Streaming over HTTP (DASH) standard is becoming increasingly popular for real-time adaptive HTTP streaming of internet video in response to unstable network conditions. Integration of DASH streaming techniques with the new H.265/HEVC video coding standard is a promising area of research. The performance of HEVC-DASH systems has been previously evaluated by a few researchers using objective metrics, however subjective evaluation would provide a better measure of the user's Quality of Experience (QoE) and overall performance of the system. This paper presents a subjective evaluation of an HEVC-DASH system implemented in a hardware testbed. Previous studies in this area have focused on using the current H.264/AVC (Advanced Video Coding) or H.264/SVC (Scalable Video Coding) codecs and moreover, there has been no established standard test procedure for the subjective evaluation of DASH adaptive streaming. In this paper, we define a test plan for HEVC-DASH with a carefully justified data set employing longer video sequences that would be sufficient to demonstrate the bitrate switching operations in response to various network condition patterns. We evaluate the end user's real-time QoE online by investigating the perceived impact of delay, different packet loss rates, fluctuating bandwidth, and the perceived quality of using different DASH video stream segment sizes on a video streaming session using different video sequences. The Mean Opinion Score (MOS) results give an insight into the performance of the system and expectation of the users. The results from this study show the impact of different network impairments and different video segments on users' QoE and further analysis and study may help in optimizing system performance.

FSM-F: Finite State Machine Based Framework for Denial of Service and Intrusion Detection in MANET

PubMed Central

N. Ahmed, Malik; Abdullah, Abdul Hanan; Kaiwartya, Omprakash

2016-01-01

Due to the continuous advancements in wireless communication in terms of quality of communication and affordability of the technology, the application area of Mobile Adhoc Networks (MANETs) significantly growing particularly in military and disaster management. Considering the sensitivity of the application areas, security in terms of detection of Denial of Service (DoS) and intrusion has become prime concern in research and development in the area. The security systems suggested in the past has state recognition problem where the system is not able to accurately identify the actual state of the network nodes due to the absence of clear definition of states of the nodes. In this context, this paper proposes a framework based on Finite State Machine (FSM) for denial of service and intrusion detection in MANETs. In particular, an Interruption Detection system for Adhoc On-demand Distance Vector (ID-AODV) protocol is presented based on finite state machine. The packet dropping and sequence number attacks are closely investigated and detection systems for both types of attacks are designed. The major functional modules of ID-AODV includes network monitoring system, finite state machine and attack detection model. Simulations are carried out in network simulator NS-2 to evaluate the performance of the proposed framework. A comparative evaluation of the performance is also performed with the state-of-the-art techniques: RIDAN and AODV. The performance evaluations attest the benefits of proposed framework in terms of providing better security for denial of service and intrusion detection attacks. PMID:27285146

An Energy Efficient Cooperative Hierarchical MIMO Clustering Scheme for Wireless Sensor Networks

PubMed Central

Nasim, Mehwish; Qaisar, Saad; Lee, Sungyoung

2012-01-01

In this work, we present an energy efficient hierarchical cooperative clustering scheme for wireless sensor networks. Communication cost is a crucial factor in depleting the energy of sensor nodes. In the proposed scheme, nodes cooperate to form clusters at each level of network hierarchy ensuring maximal coverage and minimal energy expenditure with relatively uniform distribution of load within the network. Performance is enhanced by cooperative multiple-input multiple-output (MIMO) communication ensuring energy efficiency for WSN deployments over large geographical areas. We test our scheme using TOSSIM and compare the proposed scheme with cooperative multiple-input multiple-output (CMIMO) clustering scheme and traditional multihop Single-Input-Single-Output (SISO) routing approach. Performance is evaluated on the basis of number of clusters, number of hops, energy consumption and network lifetime. Experimental results show significant energy conservation and increase in network lifetime as compared to existing schemes. PMID:22368459

NASAwide electronic publishing system: Electronic printing and duplicating, stage-2 evaluation report (GSFC)

NASA Technical Reports Server (NTRS)

Tuey, Richard C.; Lane, Robert; Hart, Susan V.

1995-01-01

The NASA Scientific and Technical Information Office was assigned the responsibility to continue with the expansion of the NASAwide networked electronic duplicating effort by including the Goddard Space Flight Center (GSFC) as an additional node to the existing configuration of networked electronic duplicating systems within NASA. The subject of this report is the evaluation of a networked electronic duplicating system which meets the duplicating requirements and expands electronic publishing capabilities without increasing current operating costs. This report continues the evaluation reported in 'NASA Electronic Publishing System - Electronic Printing and Duplicating Evaluation Report' (NASA TM-106242) and 'NASA Electronic Publishing System - Stage 1 Evaluation Report' (NASA TM-106510). This report differs from the previous reports through the inclusion of an external networked desktop editing, archival, and publishing functionality which did not exist with the previous networked electronic duplicating system. Additionally, a two-phase approach to the evaluation was undertaken; the first was a paper study justifying a 90-day, on-site evaluation, and the second phase was to validate, during the 90-day evaluation, the cost benefits and productivity increases that could be achieved in an operational mode. A benchmark of the functionality of the networked electronic publishing system and external networked desktop editing, archival, and publishing system was performed under a simulated daily production environment. This report can be used to guide others in determining the most cost effective duplicating/publishing alternative through the use of cost/benefit analysis and return on investment techniques. A treatise on the use of these techniques can be found by referring to 'NASA Electronic Publishing System -Cost/Benefit Methodology' (NASA TM-106662).

Classification of multispectral image data by the Binary Diamond neural network and by nonparametric, pixel-by-pixel methods

NASA Technical Reports Server (NTRS)

Salu, Yehuda; Tilton, James

1993-01-01

The classification of multispectral image data obtained from satellites has become an important tool for generating ground cover maps. This study deals with the application of nonparametric pixel-by-pixel classification methods in the classification of pixels, based on their multispectral data. A new neural network, the Binary Diamond, is introduced, and its performance is compared with a nearest neighbor algorithm and a back-propagation network. The Binary Diamond is a multilayer, feed-forward neural network, which learns from examples in unsupervised, 'one-shot' mode. It recruits its neurons according to the actual training set, as it learns. The comparisons of the algorithms were done by using a realistic data base, consisting of approximately 90,000 Landsat 4 Thematic Mapper pixels. The Binary Diamond and the nearest neighbor performances were close, with some advantages to the Binary Diamond. The performance of the back-propagation network lagged behind. An efficient nearest neighbor algorithm, the binned nearest neighbor, is described. Ways for improving the performances, such as merging categories, and analyzing nonboundary pixels, are addressed and evaluated.

Evaluation of Deep Learning Models for Predicting CO2 Flux

NASA Astrophysics Data System (ADS)

Halem, M.; Nguyen, P.; Frankel, D.

2017-12-01

Artificial neural networks have been employed to calculate surface flux measurements from station data because they are able to fit highly nonlinear relations between input and output variables without knowing the detail relationships between the variables. However, the accuracy in performing neural net estimates of CO2 flux from observations of CO2 and other atmospheric variables is influenced by the architecture of the neural model, the availability, and complexity of interactions between physical variables such as wind, temperature, and indirect variables like latent heat, and sensible heat, etc. We evaluate two deep learning models, feed forward and recurrent neural network models to learn how they each respond to the physical measurements, time dependency of the measurements of CO2 concentration, humidity, pressure, temperature, wind speed etc. for predicting the CO2 flux. In this paper, we focus on a) building neural network models for estimating CO2 flux based on DOE data from tower Atmospheric Radiation Measurement data; b) evaluating the impact of choosing the surface variables and model hyper-parameters on the accuracy and predictions of surface flux; c) assessing the applicability of the neural network models on estimate CO2 flux by using OCO-2 satellite data; d) studying the efficiency of using GPU-acceleration for neural network performance using IBM Power AI deep learning software and packages on IBM Minsky system.

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…

40 CFR 51.353 - Network type and program evaluation.

Code of Federal Regulations, 2013 CFR

2013-07-01

..., decentralized, or a hybrid of the two at the State's discretion, but shall be demonstrated to achieve the same... § 51.351 or 51.352 of this subpart. For decentralized programs other than those meeting the design.... (a) Presumptive equivalency. A decentralized network consisting of stations that only perform...

Optimizing performance of hybrid FSO/RF networks in realistic dynamic scenarios

NASA Astrophysics Data System (ADS)

Llorca, Jaime; Desai, Aniket; Baskaran, Eswaran; Milner, Stuart; Davis, Christopher

2005-08-01

Hybrid Free Space Optical (FSO) and Radio Frequency (RF) networks promise highly available wireless broadband connectivity and quality of service (QoS), particularly suitable for emerging network applications involving extremely high data rate transmissions such as high quality video-on-demand and real-time surveillance. FSO links are prone to atmospheric obscuration (fog, clouds, snow, etc) and are difficult to align over long distances due the use of narrow laser beams and the effect of atmospheric turbulence. These problems can be mitigated by using adjunct directional RF links, which provide backup connectivity. In this paper, methodologies for modeling and simulation of hybrid FSO/RF networks are described. Individual link propagation models are derived using scattering theory, as well as experimental measurements. MATLAB is used to generate realistic atmospheric obscuration scenarios, including moving cloud layers at different altitudes. These scenarios are then imported into a network simulator (OPNET) to emulate mobile hybrid FSO/RF networks. This framework allows accurate analysis of the effects of node mobility, atmospheric obscuration and traffic demands on network performance, and precise evaluation of topology reconfiguration algorithms as they react to dynamic changes in the network. Results show how topology reconfiguration algorithms, together with enhancements to TCP/IP protocols which reduce the network response time, enable the network to rapidly detect and act upon link state changes in highly dynamic environments, ensuring optimized network performance and availability.

The performance evaluation of a new neural network based traffic management scheme for a satellite communication network

NASA Technical Reports Server (NTRS)

Ansari, Nirwan; Liu, Dequan

1991-01-01

A neural-network-based traffic management scheme for a satellite communication network is described. The scheme consists of two levels of management. The front end of the scheme is a derivation of Kohonen's self-organization model to configure maps for the satellite communication network dynamically. The model consists of three stages. The first stage is the pattern recognition task, in which an exemplar map that best meets the current network requirements is selected. The second stage is the analysis of the discrepancy between the chosen exemplar map and the state of the network, and the adaptive modification of the chosen exemplar map to conform closely to the network requirement (input data pattern) by means of Kohonen's self-organization. On the basis of certain performance criteria, whether a new map is generated to replace the original chosen map is decided in the third stage. A state-dependent routing algorithm, which arranges the incoming call to some proper path, is used to make the network more efficient and to lower the call block rate. Simulation results demonstrate that the scheme, which combines self-organization and the state-dependent routing mechanism, provides better performance in terms of call block rate than schemes that only have either the self-organization mechanism or the routing mechanism.

Wireless sensor placement for structural monitoring using information-fusing firefly algorithm

NASA Astrophysics Data System (ADS)

Zhou, Guang-Dong; Yi, Ting-Hua; Xie, Mei-Xi; Li, Hong-Nan

2017-10-01

Wireless sensor networks (WSNs) are promising technology in structural health monitoring (SHM) applications for their low cost and high efficiency. The limited wireless sensors and restricted power resources in WSNs highlight the significance of optimal wireless sensor placement (OWSP) during designing SHM systems to enable the most useful information to be captured and to achieve the longest network lifetime. This paper presents a holistic approach, including an optimization criterion and a solution algorithm, for optimally deploying self-organizing multi-hop WSNs on large-scale structures. The combination of information effectiveness represented by the modal independence and the network performance specified by the network connectivity and network lifetime is first formulated to evaluate the performance of wireless sensor configurations. Then, an information-fusing firefly algorithm (IFFA) is developed to solve the OWSP problem. The step sizes drawn from a Lévy distribution are adopted to drive fireflies toward brighter individuals. Following the movement with Lévy flights, information about the contributions of wireless sensors to the objective function as carried by the fireflies is fused and applied to move inferior wireless sensors to better locations. The reliability of the proposed approach is verified via a numerical example on a long-span suspension bridge. The results demonstrate that the evaluation criterion provides a good performance metric of wireless sensor configurations, and the IFFA outperforms the simple discrete firefly algorithm.

Empirical Evaluation of Different Feature Representations for Social Circles Detection

DTIC Science & Technology

2015-06-16

study and compare the performance on the available labelled Facebook data from the Kaggle competition on learning social circles in networks . We...Kaggle competition on learning social circles in networks [5]. The data consist of hand- labelled friendship egonets from Facebook and a set of 57...16. SECURITY CLASSIFICATION OF: Social circles detection is a special case of community detection in social network that is currently attracting a

Recovery trajectories of kelp forest animals are rapid yet spatially variable across a network of temperate marine protected areas

NASA Astrophysics Data System (ADS)

Caselle, Jennifer E.; Rassweiler, Andrew; Hamilton, Scott L.; Warner, Robert R.

2015-09-01

Oceans currently face a variety of threats, requiring ecosystem-based approaches to management such as networks of marine protected areas (MPAs). We evaluated changes in fish biomass on temperate rocky reefs over the decade following implementation of a network of MPAs in the northern Channel Islands, California. We found that the biomass of targeted (i.e. fished) species has increased consistently inside all MPAs in the network, with an effect of geography on the strength of the response. More interesting, biomass of targeted fish species also increased outside MPAs, although only 27% as rapidly as in the protected areas, indicating that redistribution of fishing effort has not severely affected unprotected populations. Whether the increase outside of MPAs is due to changes in fishing pressure, fisheries management actions, adult spillover, favorable environmental conditions, or a combination of all four remains unknown. We evaluated methods of controlling for biogeographic or environmental variation across networks of protected areas and found similar performance of models incorporating empirical sea surface temperature versus a simple geographic blocking term based on assemblage structure. The patterns observed are promising indicators of the success of this network, but more work is needed to understand how ecological and physical contexts affect MPA performance.

Recovery trajectories of kelp forest animals are rapid yet spatially variable across a network of temperate marine protected areas

PubMed Central

Caselle, Jennifer E.; Rassweiler, Andrew; Hamilton, Scott L.; Warner, Robert R.

2015-01-01

Oceans currently face a variety of threats, requiring ecosystem-based approaches to management such as networks of marine protected areas (MPAs). We evaluated changes in fish biomass on temperate rocky reefs over the decade following implementation of a network of MPAs in the northern Channel Islands, California. We found that the biomass of targeted (i.e. fished) species has increased consistently inside all MPAs in the network, with an effect of geography on the strength of the response. More interesting, biomass of targeted fish species also increased outside MPAs, although only 27% as rapidly as in the protected areas, indicating that redistribution of fishing effort has not severely affected unprotected populations. Whether the increase outside of MPAs is due to changes in fishing pressure, fisheries management actions, adult spillover, favorable environmental conditions, or a combination of all four remains unknown. We evaluated methods of controlling for biogeographic or environmental variation across networks of protected areas and found similar performance of models incorporating empirical sea surface temperature versus a simple geographic blocking term based on assemblage structure. The patterns observed are promising indicators of the success of this network, but more work is needed to understand how ecological and physical contexts affect MPA performance. PMID:26373803

A Neural Relevance Model for Feature Extraction from Hyperspectral Images, and Its Application in the Wavelet Domain

DTIC Science & Technology

2006-08-01

Nikolas Avouris. Evaluation of classifiers for an uneven class distribution problem. Applied Artificial Intellegence , pages 1-24, 2006. Draft manuscript...data by a hybrid artificial neural network so we may evaluate the classification capabilities of the baseline GRLVQ and our improved GRLVQI. Chapter 4...performance of GRLVQ(I), we compare the results against a baseline classification of the 23-class problem with a hybrid artificial neural network (ANN

Public–nonprofit partnership performance in a disaster context: the case of Haiti.

PubMed

Nolte, Isabella M; Boenigk, Silke

2011-01-01

During disasters, partnerships between public and nonprofit organizations are vital to provide fast relief to affected communities. In this article, we develop a process model to support a performance evaluation of such intersectoral partnerships. The model includes input factors, organizational structures, outputs and the long-term outcomes of public–nonprofit partnerships. These factors derive from theory and a systematic literature review of emergency, public, nonprofit, and network research. To adapt the model to a disaster context, we conducted a case study that examines public and nonprofit organizations that partnered during the 2010 Haiti earthquake. The case study results show that communication, trust, and experience are the most important partnership inputs; the most prevalent governance structure of public–nonprofit partnerships is a lead organization network. Time and quality measures should be considered to assess partnership outputs, and community, network, and organizational actor perspectives must be taken into account when evaluating partnership outcomes.

Mild traumatic brain injury: graph-model characterization of brain networks for episodic memory.

PubMed

Tsirka, Vasso; Simos, Panagiotis G; Vakis, Antonios; Kanatsouli, Kassiani; Vourkas, Michael; Erimaki, Sofia; Pachou, Ellie; Stam, Cornelis Jan; Micheloyannis, Sifis

2011-02-01

Episodic memory is among the cognitive functions that can be affected in the acute phase following mild traumatic brain injury (MTBI). The present study used EEG recordings to evaluate global synchronization and network organization of rhythmic activity during the encoding and recognition phases of an episodic memory task varying in stimulus type (kaleidoscope images, pictures, words, and pseudowords). Synchronization of oscillatory activity was assessed using a linear and nonlinear connectivity estimator and network analyses were performed using algorithms derived from graph theory. Twenty five MTBI patients (tested within days post-injury) and healthy volunteers were closely matched on demographic variables, verbal ability, psychological status variables, as well as on overall task performance. Patients demonstrated sub-optimal network organization, as reflected by changes in graph parameters in the theta and alpha bands during both encoding and recognition. There were no group differences in spectral energy during task performance or on network parameters during a control condition (rest). Evidence of less optimally organized functional networks during memory tasks was more prominent for pictorial than for verbal stimuli. Copyright © 2010 Elsevier B.V. All rights reserved.

Neural network versus classical time series forecasting models

NASA Astrophysics Data System (ADS)

Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam

2017-05-01

Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.

A collaborative computing framework of cloud network and WBSN applied to fall detection and 3-D motion reconstruction.

PubMed

Lai, Chin-Feng; Chen, Min; Pan, Jeng-Shyang; Youn, Chan-Hyun; Chao, Han-Chieh

2014-03-01

As cloud computing and wireless body sensor network technologies become gradually developed, ubiquitous healthcare services prevent accidents instantly and effectively, as well as provides relevant information to reduce related processing time and cost. This study proposes a co-processing intermediary framework integrated cloud and wireless body sensor networks, which is mainly applied to fall detection and 3-D motion reconstruction. In this study, the main focuses includes distributed computing and resource allocation of processing sensing data over the computing architecture, network conditions and performance evaluation. Through this framework, the transmissions and computing time of sensing data are reduced to enhance overall performance for the services of fall events detection and 3-D motion reconstruction.

GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection

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

Harshaw, Chris R; Bridges, Robert A; Iannacone, Michael D

This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called \\textit{GraphPrints}. Building on foundational network-mining techniques, our method represents time slices of traffic as a graph, then counts graphlets\\textemdash small induced subgraphs that describe local topology. By performing outlier detection on the sequence of graphlet counts, anomalous intervals of traffic are identified, and furthermore, individual IPs experiencing abnormal behavior are singled-out. Initial testing of GraphPrints is performed on real network data with an implanted anomaly. Evaluation shows false positive rates bounded by 2.84\\% at the time-interval level, and 0.05\\% at the IP-level with 100\\% truemore » positive rates at both.« less

On the relevance of using open wireless sensor networks in environment monitoring.

PubMed

Bagula, Antoine B; Inggs, Gordon; Scott, Simon; Zennaro, Marco

2009-01-01

This paper revisits the problem of the readiness for field deployments of wireless sensor networks by assessing the relevance of using Open Hardware and Software motes for environment monitoring. We propose a new prototype wireless sensor network that fine-tunes SquidBee motes to improve the life-time and sensing performance of an environment monitoring system that measures temperature, humidity and luminosity. Building upon two outdoor sensing scenarios, we evaluate the performance of the newly proposed energy-aware prototype solution in terms of link quality when expressed by the Received Signal Strength, Packet Loss and the battery lifetime. The experimental results reveal the relevance of using the Open Hardware and Software motes when setting up outdoor wireless sensor networks.

Impact of window decrement rate on TCP performance in an adhoc network

NASA Astrophysics Data System (ADS)

Suherman; Hutasuhut, Arief T. W.; Badra, Khaldun; Al-Akaidi, Marwan

2017-09-01

Transmission control protocol (TCP) is a reliable transport protocol handling end to end connection in TCP/IP stack. It works well in copper or optical fibre link, but experiences increasing delay in wireless network. Further, TCP experiences multiple retransmissions due to higher collision probability within wireless network. The situation may get worsen in an ad hoc network. This paper examines the impact half window or window reduction rate to the overall TCP performances. The evaluation using NS-2 simulator shows that the smaller the window decrement rate results the smaller end to end delay. Delay is reduced to 17.05% in average when window decrement rate decreases. Average jitter also decreases 4.15%, while packet loss is not affected.

Directional virtual backbone based data aggregation scheme for Wireless Visual Sensor Networks.

PubMed

Zhang, Jing; Liu, Shi-Jian; Tsai, Pei-Wei; Zou, Fu-Min; Ji, Xiao-Rong

2018-01-01

Data gathering is a fundamental task in Wireless Visual Sensor Networks (WVSNs). Features of directional antennas and the visual data make WVSNs more complex than the conventional Wireless Sensor Network (WSN). The virtual backbone is a technique, which is capable of constructing clusters. The version associating with the aggregation operation is also referred to as the virtual backbone tree. In most of the existing literature, the main focus is on the efficiency brought by the construction of clusters that the existing methods neglect local-balance problems in general. To fill up this gap, Directional Virtual Backbone based Data Aggregation Scheme (DVBDAS) for the WVSNs is proposed in this paper. In addition, a measurement called the energy consumption density is proposed for evaluating the adequacy of results in the cluster-based construction problems. Moreover, the directional virtual backbone construction scheme is proposed by considering the local-balanced factor. Furthermore, the associated network coding mechanism is utilized to construct DVBDAS. Finally, both the theoretical analysis of the proposed DVBDAS and the simulations are given for evaluating the performance. The experimental results prove that the proposed DVBDAS achieves higher performance in terms of both the energy preservation and the network lifetime extension than the existing methods.

An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network.

PubMed

Shen, Xiaolei; Zhang, Jiachi; Yan, Chenjun; Zhou, Hong

2018-04-11

In this paper, we present a new automatic diagnosis method for facial acne vulgaris which is based on convolutional neural networks (CNNs). To overcome the shortcomings of previous methods which were the inability to classify enough types of acne vulgaris. The core of our method is to extract features of images based on CNNs and achieve classification by classifier. A binary-classifier of skin-and-non-skin is used to detect skin area and a seven-classifier is used to achieve the classification task of facial acne vulgaris and healthy skin. In the experiments, we compare the effectiveness of our CNN and the VGG16 neural network which is pre-trained on the ImageNet data set. We use a ROC curve to evaluate the performance of binary-classifier and use a normalized confusion matrix to evaluate the performance of seven-classifier. The results of our experiments show that the pre-trained VGG16 neural network is effective in extracting features from facial acne vulgaris images. And the features are very useful for the follow-up classifiers. Finally, we try applying the classifiers both based on the pre-trained VGG16 neural network to assist doctors in facial acne vulgaris diagnosis.

A laboratory system for the investigation of rain fade compensation techniques for Ka-band satellites

NASA Technical Reports Server (NTRS)

Svoboda, James S.; Kachmar, Brian A.

1993-01-01

The design and performance of a rain fade simulation/counteraction system on a laboratory simulated 30/20 GHz, time division multiple access (TDMA) satellite communications testbed is evaluated. Severe rain attenuation of electromagnetic radiation at 30/20 GHz occurs due to the carrier wavelength approaching the water droplet size. Rain in the downlink path lowers the signal power present at the receiver, resulting in a higher number of bit errors induced in the digital ground terminal. The laboratory simulation performed at NASA Lewis Research Center uses a programmable PIN diode attenuator to simulate 20 GHz satellite downlink geographic rain fade profiles. A computer based network control system monitors the downlink power and informs the network of any power threshold violations, which then prompts the network to issue commands that temporarily increase the gain of the satellite based traveling wave tube (TWT) amplifier. After the rain subsides, the network returns the TWT to the normal energy conserving power mode. Bit error rate (BER) data taken at the receiving ground terminal serves as a measure of the severity of rain degradation, and also evaluates the extent to which the network can improve the faded channel.

Integrated RF/Optical Interplanetary Networking Preliminary Explorations and Empirical Results

NASA Technical Reports Server (NTRS)

Raible, Daniel E.; Hylton, Alan G.

2012-01-01

Over the last decade interplanetary telecommunication capabilities have been significantly expanded--specifically in support of the Mars exploration rover and lander missions. NASA is continuing to drive advances in new, high payoff optical communications technologies to enhance the network to Gbps performance from Mars, and the transition from technology demonstration to operational system is examined through a hybrid RF/optical approach. Such a system combines the best features of RF and optical communications considering availability and performance to realize a dual band trunk line operating within characteristic constraints. Disconnection due to planetary obscuration and solar conjunction, link delays, timing, ground terminal mission congestion and scheduling policy along with space and atmospheric weather disruptions all imply the need for network protocol solutions to ultimately manage the physical layer in a transparent manner to the end user. Delay Tolerant Networking (DTN) is an approach under evaluation which addresses these challenges. A multi-hop multi-path hybrid RF and optical test bed has been constructed to emulate the integrated deep space network and to support protocol and hardware refinement. Initial experimental results characterize several of these challenges and evaluate the effectiveness of DTN as a solution to mitigate them.

Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring

NASA Technical Reports Server (NTRS)

Trejo, Leonard J.; Shensa, Mark J.; Remington, Roger W. (Technical Monitor)

1998-01-01

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many f ree parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation,-, algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

NASA Technical Reports Server (NTRS)

Trejo, L. J.; Shensa, M. J.

1999-01-01

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

PubMed Central

Ayton, Ellyn; Porterfield, Katherine; Corley, Courtney D.

2017-01-01

This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in “real-time”) and forecasting (predicting the future) ILI dynamics in the 2011 – 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a) Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns. (g) Model performance improves with more tweets available per geo-location e.g., the error gets lower and the Pearson score gets higher for locations with more tweets. PMID:29244814

Forecasting influenza-like illness dynamics for military populations using neural networks and social media.

PubMed

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine; Corley, Courtney D

2017-01-01

This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in "real-time") and forecasting (predicting the future) ILI dynamics in the 2011 - 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a) Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns. (g) Model performance improves with more tweets available per geo-location e.g., the error gets lower and the Pearson score gets higher for locations with more tweets.

Space network scheduling benchmark: A proof-of-concept process for technology transfer

NASA Technical Reports Server (NTRS)

Moe, Karen; Happell, Nadine; Hayden, B. J.; Barclay, Cathy

1993-01-01

This paper describes a detailed proof-of-concept activity to evaluate flexible scheduling technology as implemented in the Request Oriented Scheduling Engine (ROSE) and applied to Space Network (SN) scheduling. The criteria developed for an operational evaluation of a reusable scheduling system is addressed including a methodology to prove that the proposed system performs at least as well as the current system in function and performance. The improvement of the new technology must be demonstrated and evaluated against the cost of making changes. Finally, there is a need to show significant improvement in SN operational procedures. Successful completion of a proof-of-concept would eventually lead to an operational concept and implementation transition plan, which is outside the scope of this paper. However, a high-fidelity benchmark using actual SN scheduling requests has been designed to test the ROSE scheduling tool. The benchmark evaluation methodology, scheduling data, and preliminary results are described.

Real-time networked control of an industrial robot manipulator via discrete-time second-order sliding modes

NASA Astrophysics Data System (ADS)

Massimiliano Capisani, Luca; Facchinetti, Tullio; Ferrara, Antonella

2010-08-01

This article presents the networked control of a robotic anthropomorphic manipulator based on a second-order sliding mode technique, where the control objective is to track a desired trajectory for the manipulator. The adopted control scheme allows an easy and effective distribution of the control algorithm over two networked machines. While the predictability of real-time tasks execution is achieved by the Soft Hard Real-Time Kernel (S.Ha.R.K.) real-time operating system, the communication is established via a standard Ethernet network. The performances of the control system are evaluated under different experimental system configurations using, to perform the experiments, a COMAU SMART3-S2 industrial robot, and the results are analysed to put into evidence the robustness of the proposed approach against possible network delays, packet losses and unmodelled effects.

Errors on interrupter tasks presented during spatial and verbal working memory performance are linearly linked to large-scale functional network connectivity in high temporal resolution resting state fMRI.

PubMed

Magnuson, Matthew Evan; Thompson, Garth John; Schwarb, Hillary; Pan, Wen-Ju; McKinley, Andy; Schumacher, Eric H; Keilholz, Shella Dawn

2015-12-01

The brain is organized into networks composed of spatially separated anatomical regions exhibiting coherent functional activity over time. Two of these networks (the default mode network, DMN, and the task positive network, TPN) have been implicated in the performance of a number of cognitive tasks. To directly examine the stable relationship between network connectivity and behavioral performance, high temporal resolution functional magnetic resonance imaging (fMRI) data were collected during the resting state, and behavioral data were collected from 15 subjects on different days, exploring verbal working memory, spatial working memory, and fluid intelligence. Sustained attention performance was also evaluated in a task interleaved between resting state scans. Functional connectivity within and between the DMN and TPN was related to performance on these tasks. Decreased TPN resting state connectivity was found to significantly correlate with fewer errors on an interrupter task presented during a spatial working memory paradigm and decreased DMN/TPN anti-correlation was significantly correlated with fewer errors on an interrupter task presented during a verbal working memory paradigm. A trend for increased DMN resting state connectivity to correlate to measures of fluid intelligence was also observed. These results provide additional evidence of the relationship between resting state networks and behavioral performance, and show that such results can be observed with high temporal resolution fMRI. Because cognitive scores and functional connectivity were collected on nonconsecutive days, these results highlight the stability of functional connectivity/cognitive performance coupling.

A prediction model for cognitive performance in health ageing using diffusion tensor imaging with graph theory.

PubMed

Yun, Ruijuan; Lin, Chung-Chih; Wu, Shuicai; Huang, Chu-Chung; Lin, Ching-Po; Chao, Yi-Ping

2013-01-01

In this study, we employed diffusion tensor imaging (DTI) to construct brain structural network and then derive the connection matrices from 96 healthy elderly subjects. The correlation analysis between these topological properties of network based on graph theory and the Cognitive Abilities Screening Instrument (CASI) index were processed to extract the significant network characteristics. These characteristics were then integrated to estimate the models by various machine-learning algorithms to predict user's cognitive performance. From the results, linear regression model and Gaussian processes model showed presented better abilities with lower mean absolute errors of 5.8120 and 6.25 to predict the cognitive performance respectively. Moreover, these extracted topological properties of brain structural network derived from DTI also could be regarded as the bio-signatures for further evaluation of brain degeneration in healthy aged and early diagnosis of mild cognitive impairment (MCI).

Schemes for efficient transmission of encoded video streams on high-speed networks

NASA Astrophysics Data System (ADS)

Ramanathan, Srinivas; Vin, Harrick M.; Rangan, P. Venkat

1994-04-01

In this paper, we argue that significant performance benefits can accrue if integrated networks implement application-specific mechanisms that account for the diversities in media compression schemes. Towards this end, we propose a simple, yet effective, strategy called Frame Induced Packet Discarding (FIPD), in which, upon detection of loss of a threshold number (determined by an application's video encoding scheme) of packets belonging to a video frame, the network attempts to discard all the remaining packets of that frame. In order to analytically quantify the performance of FIPD so as to obtain fractional frame losses that can be guaranteed to video channels, we develop a finite state, discrete time markov chain model of the FIPD strategy. The fractional frame loss thus computed can serve as the criterion for admission control at the network. Performance evaluations demonstrate the utility of the FIPD strategy.

Interconnect Performance Evaluation of SGI Altix 3700 BX2, Cray X1, Cray Opteron Cluster, and Dell PowerEdge

NASA Technical Reports Server (NTRS)

Fatoohi, Rod; Saini, Subbash; Ciotti, Robert

2006-01-01

We study the performance of inter-process communication on four high-speed multiprocessor systems using a set of communication benchmarks. The goal is to identify certain limiting factors and bottlenecks with the interconnect of these systems as well as to compare these interconnects. We measured network bandwidth using different number of communicating processors and communication patterns, such as point-to-point communication, collective communication, and dense communication patterns. The four platforms are: a 512-processor SGI Altix 3700 BX2 shared-memory machine with 3.2 GB/s links; a 64-processor (single-streaming) Cray XI shared-memory machine with 32 1.6 GB/s links; a 128-processor Cray Opteron cluster using a Myrinet network; and a 1280-node Dell PowerEdge cluster with an InfiniBand network. Our, results show the impact of the network bandwidth and topology on the overall performance of each interconnect.

Design and performance evaluation of an OpenFlow-based control plane for software-defined elastic optical networks with direct-detection optical OFDM (DDO-OFDM) transmission.

PubMed

Liu, Lei; Peng, Wei-Ren; Casellas, Ramon; Tsuritani, Takehiro; Morita, Itsuro; Martínez, Ricardo; Muñoz, Raül; Yoo, S J B

2014-01-13

Optical Orthogonal Frequency Division Multiplexing (O-OFDM), which transmits high speed optical signals using multiple spectrally overlapped lower-speed subcarriers, is a promising candidate for supporting future elastic optical networks. In contrast to previous works which focus on Coherent Optical OFDM (CO-OFDM), in this paper, we consider the direct-detection optical OFDM (DDO-OFDM) as the transport technique, which leads to simpler hardware and software realizations, potentially offering a low-cost solution for elastic optical networks, especially in metro networks, and short or medium distance core networks. Based on this network scenario, we design and deploy a software-defined networking (SDN) control plane enabled by extending OpenFlow, detailing the network architecture, the routing and spectrum assignment algorithm, OpenFlow protocol extensions and the experimental validation. To the best of our knowledge, it is the first time that an OpenFlow-based control plane is reported and its performance is quantitatively measured in an elastic optical network with DDO-OFDM transmission.

SigFlux: a novel network feature to evaluate the importance of proteins in signal transduction networks.

PubMed

Liu, Wei; Li, Dong; Zhang, Jiyang; Zhu, Yunping; He, Fuchu

2006-11-27

Measuring each protein's importance in signaling networks helps to identify the crucial proteins in a cellular process, find the fragile portion of the biology system and further assist for disease therapy. However, there are relatively few methods to evaluate the importance of proteins in signaling networks. We developed a novel network feature to evaluate the importance of proteins in signal transduction networks, that we call SigFlux, based on the concept of minimal path sets (MPSs). An MPS is a minimal set of nodes that can perform the signal propagation from ligands to target genes or feedback loops. We define SigFlux as the number of MPSs in which each protein is involved. We applied this network feature to the large signal transduction network in the hippocampal CA1 neuron of mice. Significant correlations were simultaneously observed between SigFlux and both the essentiality and evolutionary rate of genes. Compared with another commonly used network feature, connectivity, SigFlux has similar or better ability as connectivity to reflect a protein's essentiality. Further classification according to protein function demonstrates that high SigFlux, low connectivity proteins are abundant in receptors and transcriptional factors, indicating that SigFlux candescribe the importance of proteins within the context of the entire network. SigFlux is a useful network feature in signal transduction networks that allows the prediction of the essentiality and conservation of proteins. With this novel network feature, proteins that participate in more pathways or feedback loops within a signaling network are proved far more likely to be essential and conserved during evolution than their counterparts.

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

Modeling and simulation of the data communication network at the ASRM Facility

NASA Technical Reports Server (NTRS)

Nirgudkar, R. P.; Moorhead, R. J.; Smith, W. D.

1994-01-01

This paper describes the modeling and simulation of the communication network for the NASA Advanced Solid Rocket Motor (ASRM) facility under construction at Yellow Creek near Luka, Mississippi. Manufacturing, testing, and operations at the ASRM site will be performed in different buildings scattered over an 1800 acre site. These buildings are interconnected through a local area network (LAN), which will contain one logical Fiber Distributed Data Interface (FDDI) ring acting as a backbone for the whole complex. The network contains approximately 700 multi-vendor workstations, 22 multi-vendor workcells, and 3 VAX clusters interconnected via Ethernet and FDDI. The different devices produce appreciably different traffic patterns, each pattern will be highly variable, and some patterns will be very bursty. Most traffic is between the VAX clusters and the other devices. Comdisco's Block Oriented Network Simulator (BONeS) has been used for network simulation. The two primary evaluation parameters used to judge the expected network performance are throughput and delay.

Design and Benchmarking of a Network-In-the-Loop Simulation for Use in a Hardware-In-the-Loop System

NASA Technical Reports Server (NTRS)

Aretskin-Hariton, Eliot; Thomas, George; Culley, Dennis; Kratz, Jonathan

2017-01-01

Distributed engine control (DEC) systems alter aircraft engine design constraints because of fundamental differences in the input and output communication between DEC and centralized control architectures. The change in the way communication is implemented may create new optimum engine-aircraft configurations. This paper continues the exploration of digital network communication by demonstrating a Network-In-the-Loop simulation at the NASA Glenn Research Center. This simulation incorporates a real-time network protocol, the Engine Area Distributed Interconnect Network Lite (EADIN Lite), with the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) software. The objective of this study is to assess digital control network impact to the control system. Performance is evaluated relative to a truth model for large transient maneuvers and a typical flight profile for commercial aircraft. Results show that a decrease in network bandwidth from 250 Kbps (sampling all sensors every time step) to 40 Kbps, resulted in very small differences in control system performance.

Design and Benchmarking of a Network-In-the-Loop Simulation for Use in a Hardware-In-the-Loop System

NASA Technical Reports Server (NTRS)

Aretskin-Hariton, Eliot D.; Thomas, George Lindsey; Culley, Dennis E.; Kratz, Jonathan L.

2017-01-01

Distributed engine control (DEC) systems alter aircraft engine design constraints be- cause of fundamental differences in the input and output communication between DEC and centralized control architectures. The change in the way communication is implemented may create new optimum engine-aircraft configurations. This paper continues the exploration of digital network communication by demonstrating a Network-In-the-Loop simulation at the NASA Glenn Research Center. This simulation incorporates a real-time network protocol, the Engine Area Distributed Interconnect Network Lite (EADIN Lite), with the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) software. The objective of this study is to assess digital control network impact to the control system. Performance is evaluated relative to a truth model for large transient maneuvers and a typical flight profile for commercial aircraft. Results show that a decrease in network bandwidth from 250 Kbps (sampling all sensors every time step) to 40 Kbps, resulted in very small differences in control system performance.

Integration of Virtual Machine Technologies into Hastily Formed Networks in Support of Humanitarian Relief and Disaster Recovery Missions

DTIC Science & Technology

2011-12-01

and measures of effectiveness (MOE). New technologies that offer solid-state hard drives built into modular VDI devices known as appliances ...Joint Reconfigurable Vehicle LAN Local Area Network LOS Line of Sight LTE Long Term Evolution MB Megabyte MOP Measure of Performance MOE Measure ...re-usable measures of performance and measures of effectiveness (MOP and MOE) and evaluation procedures will be applied to this research. A

Link Investigation of IEEE 802.15.4 Wireless Sensor Networks in Forests

PubMed Central

Ding, Xingjian; Sun, Guodong; Yang, Gaoxiang; Shang, Xinna

2016-01-01

Wireless sensor networks are expected to automatically monitor the ecological evolution and wildlife habits in forests. Low-power links (transceivers) are often adopted in wireless sensor network applications, in order to save the precious sensor energy and then achieve long-term, unattended monitoring. Recent research has presented some performance characteristics of such low-power wireless links under laboratory or outdoor scenarios with less obstacles, and they have found that low-power wireless links are unreliable and prone to be affected by the target environment. However, there is still less understanding about how well the low-power wireless link performs in real-world forests and to what extent the complex in-forest surrounding environments affect the link performances. In this paper, we empirically evaluate the low-power links of wireless sensors in three typical different forest environments. Our experiment investigates the performance of the link layer compatible with the IEEE 802.15.4 standard and analyzes the variation patterns of the packet reception ratio (PRR), the received signal strength indicator (RSSI) and the link quality indicator (LQI) under diverse experimental settings. Some observations of this study are inconsistent with or even contradict prior results that are achieved in open fields or relatively clean environments and thus, provide new insights both into effectively evaluating the low-power wireless links and into efficiently deploying wireless sensor network systems in forest environments. PMID:27355957

Link Investigation of IEEE 802.15.4 Wireless Sensor Networks in Forests.

PubMed

Ding, Xingjian; Sun, Guodong; Yang, Gaoxiang; Shang, Xinna

2016-06-27

Wireless sensor networks are expected to automatically monitor the ecological evolution and wildlife habits in forests. Low-power links (transceivers) are often adopted in wireless sensor network applications, in order to save the precious sensor energy and then achieve long-term, unattended monitoring. Recent research has presented some performance characteristics of such low-power wireless links under laboratory or outdoor scenarios with less obstacles, and they have found that low-power wireless links are unreliable and prone to be affected by the target environment. However, there is still less understanding about how well the low-power wireless link performs in real-world forests and to what extent the complex in-forest surrounding environments affect the link performances. In this paper, we empirically evaluate the low-power links of wireless sensors in three typical different forest environments. Our experiment investigates the performance of the link layer compatible with the IEEE 802.15.4 standard and analyzes the variation patterns of the packet reception ratio (PRR), the received signal strength indicator (RSSI) and the link quality indicator (LQI) under diverse experimental settings. Some observations of this study are inconsistent with or even contradict prior results that are achieved in open fields or relatively clean environments and thus, provide new insights both into effectively evaluating the low-power wireless links and into efficiently deploying wireless sensor network systems in forest environments.

Analysis of physical layer performance of hybrid optical-wireless access network

NASA Astrophysics Data System (ADS)

Shaddad, R. Q.; Mohammad, A. B.; Al-hetar, A. M.

2011-09-01

The hybrid optical-wireless access network (HOWAN) is a favorable architecture for next generation access network. It is an optimal combination of an optical backhaul and a wireless front-end for an efficient access network. In this paper, the HOWAN architecture is designed based on a wavelengths division multiplexing/time division multiplexing passive optical network (WDM/TDM PON) at the optical backhaul and a wireless fidelity (WiFi) technology at the wireless front-end. The HOWAN is proposed that can provide blanket coverage of broadband and flexible connection for end-users. Most of the existing works, based on performance evaluation are concerned on network layer aspects. This paper reports physical layer performance in terms of the bit error rate (BER), eye diagram, and signal-to-noise ratio (SNR) of the communication system. It accommodates 8 wavelength channels with 32 optical network unit/wireless access points (ONU/APs). It is demonstrated that downstream and upstream of 2 Gb/s can be achieved by optical backhaul for each wavelength channel along optical fiber length of 20 km and a data rate of 54 Mb/s per ONU/AP along a 50 m outdoor wireless link.

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).

A Study of LoRa: Long Range & Low Power Networks for the Internet of Things

PubMed Central

Augustin, Aloÿs; Yi, Jiazi; Clausen, Thomas; Townsley, William Mark

2016-01-01

LoRa is a long-range, low-power, low-bitrate, wireless telecommunications system, promoted as an infrastructure solution for the Internet of Things: end-devices use LoRa across a single wireless hop to communicate to gateway(s), connected to the Internet and which act as transparent bridges and relay messages between these end-devices and a central network server. This paper provides an overview of LoRa and an in-depth analysis of its functional components. The physical and data link layer performance is evaluated by field tests and simulations. Based on the analysis and evaluations, some possible solutions for performance enhancements are proposed. PMID:27618064

A Study of LoRa: Long Range & Low Power Networks for the Internet of Things.

PubMed

Augustin, Aloÿs; Yi, Jiazi; Clausen, Thomas; Townsley, William Mark

2016-09-09

LoRa is a long-range, low-power, low-bitrate, wireless telecommunications system, promoted as an infrastructure solution for the Internet of Things: end-devices use LoRa across a single wireless hop to communicate to gateway(s), connected to the Internet and which act as transparent bridges and relay messages between these end-devices and a central network server. This paper provides an overview of LoRa and an in-depth analysis of its functional components. The physical and data link layer performance is evaluated by field tests and simulations. Based on the analysis and evaluations, some possible solutions for performance enhancements are proposed.

FPGA Based Reconfigurable ATM Switch Test Bed

NASA Technical Reports Server (NTRS)

Chu, Pong P.; Jones, Robert E.

1998-01-01

Various issues associated with "FPGA Based Reconfigurable ATM Switch Test Bed" are presented in viewgraph form. Specific topics include: 1) Network performance evaluation; 2) traditional approaches; 3) software simulation; 4) hardware emulation; 5) test bed highlights; 6) design environment; 7) test bed architecture; 8) abstract sheared-memory switch; 9) detailed switch diagram; 10) traffic generator; 11) data collection circuit and user interface; 12) initial results; and 13) the following conclusions: Advances in FPGA make hardware emulation feasible for performance evaluation, hardware emulation can provide several orders of magnitude speed-up over software simulation; due to the complexity of hardware synthesis process, development in emulation is much more difficult than simulation and requires knowledge in both networks and digital design.

Empirical evaluation of H.265/HEVC-based dynamic adaptive video streaming over HTTP (HEVC-DASH)

NASA Astrophysics Data System (ADS)

Irondi, Iheanyi; Wang, Qi; Grecos, Christos

2014-05-01

Real-time HTTP streaming has gained global popularity for delivering video content over Internet. In particular, the recent MPEG-DASH (Dynamic Adaptive Streaming over HTTP) standard enables on-demand, live, and adaptive Internet streaming in response to network bandwidth fluctuations. Meanwhile, emerging is the new-generation video coding standard, H.265/HEVC (High Efficiency Video Coding) promises to reduce the bandwidth requirement by 50% at the same video quality when compared with the current H.264/AVC standard. However, little existing work has addressed the integration of the DASH and HEVC standards, let alone empirical performance evaluation of such systems. This paper presents an experimental HEVC-DASH system, which is a pull-based adaptive streaming solution that delivers HEVC-coded video content through conventional HTTP servers where the client switches to its desired quality, resolution or bitrate based on the available network bandwidth. Previous studies in DASH have focused on H.264/AVC, whereas we present an empirical evaluation of the HEVC-DASH system by implementing a real-world test bed, which consists of an Apache HTTP Server with GPAC, an MP4Client (GPAC) with open HEVC-based DASH client and a NETEM box in the middle emulating different network conditions. We investigate and analyze the performance of HEVC-DASH by exploring the impact of various network conditions such as packet loss, bandwidth and delay on video quality. Furthermore, we compare the Intra and Random Access profiles of HEVC coding with the Intra profile of H.264/AVC when the correspondingly encoded video is streamed with DASH. Finally, we explore the correlation among the quality metrics and network conditions, and empirically establish under which conditions the different codecs can provide satisfactory performance.

Neural network based system for equipment surveillance

DOEpatents

Vilim, Richard B.; Gross, Kenneth C.; Wegerich, Stephan W.

1998-01-01

A method and system for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process.

Neural network based system for equipment surveillance

DOEpatents

Vilim, R.B.; Gross, K.C.; Wegerich, S.W.

1998-04-28

A method and system are disclosed for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process. 33 figs.

Experimental Evaluation of a Structure-Based Connectionist Network for Fault Diagnosis of Helicopter Gearboxes

NASA Technical Reports Server (NTRS)

Jammu, V. B.; Danai, K.; Lewicki, D. G.

1998-01-01

This paper presents the experimental evaluation of the Structure-Based Connectionist Network (SBCN) fault diagnostic system introduced in the preceding article. For this vibration data from two different helicopter gearboxes: OH-58A and S-61, are used. A salient feature of SBCN is its reliance on the knowledge of the gearbox structure and the type of features obtained from processed vibration signals as a substitute to training. To formulate this knowledge, approximate vibration transfer models are developed for the two gearboxes and utilized to derive the connection weights representing the influence of component faults on vibration features. The validity of the structural influences is evaluated by comparing them with those obtained from experimental RMS values. These influences are also evaluated ba comparing them with the weights of a connectionist network trained though supervised learning. The results indicate general agreement between the modeled and experimentally obtained influences. The vibration data from the two gearboxes are also used to evaluate the performance of SBCN in fault diagnosis. The diagnostic results indicate that the SBCN is effective in directing the presence of faults and isolating them within gearbox subsystems based on structural influences, but its performance is not as good in isolating faulty components, mainly due to lack of appropriate vibration features.

A Proposal for Modeling Real Hardware, Weather and Marine Conditions for Underwater Sensor Networks

PubMed Central

Climent, Salvador; Capella, Juan Vicente; Blanc, Sara; Perles, Angel; Serrano, Juan José

2013-01-01

Network simulators are useful for researching protocol performance, appraising new hardware capabilities and evaluating real application scenarios. However, these tasks can only be achieved when using accurate models and real parameters that enable the extraction of trustworthy results and conclusions. This paper presents an underwater wireless sensor network ecosystem for the ns-3 simulator. This ecosystem is composed of a new energy-harvesting model and a low-cost, low-power underwater wake-up modem model that, alongside existing models, enables the performance of accurate simulations by providing real weather and marine conditions from the location where the real application is to be deployed. PMID:23748171

Applying a rateless code in content delivery networks

NASA Astrophysics Data System (ADS)

Suherman; Zarlis, Muhammad; Parulian Sitorus, Sahat; Al-Akaidi, Marwan

2017-09-01

Content delivery network (CDN) allows internet providers to locate their services, to map their coverage into networks without necessarily to own them. CDN is part of the current internet infrastructures, supporting multi server applications especially social media. Various works have been proposed to improve CDN performances. Since accesses on social media servers tend to be short but frequent, providing redundant to the transmitted packets to ensure lost packets not degrade the information integrity may improve service performances. This paper examines the implementation of rateless code in the CDN infrastructure. The NS-2 evaluations show that rateless code is able to reduce packet loss up to 50%.

Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics.

PubMed

Trainor, Patrick J; DeFilippis, Andrew P; Rai, Shesh N

2017-06-21

Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics datasets, comparing Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA, Random Forests, Support Vector Machines (SVM), Artificial Neural Network, k -Nearest Neighbors ( k -NN), and Naïve Bayes classification techniques for discrimination. We evaluated the techniques on simulated data generated to mimic global untargeted metabolomics data by incorporating realistic block-wise correlation and partial correlation structures for mimicking the correlations and metabolite clustering generated by biological processes. Over the simulation studies, covariance structures, means, and effect sizes were stochastically varied to provide consistent estimates of classifier performance over a wide range of possible scenarios. The effects of the presence of non-normal error distributions, the introduction of biological and technical outliers, unbalanced phenotype allocation, missing values due to abundances below a limit of detection, and the effect of prior-significance filtering (dimension reduction) were evaluated via simulation. In each simulation, classifier parameters, such as the number of hidden nodes in a Neural Network, were optimized by cross-validation to minimize the probability of detecting spurious results due to poorly tuned classifiers. Classifier performance was then evaluated using real metabolomics datasets of varying sample medium, sample size, and experimental design. We report that in the most realistic simulation studies that incorporated non-normal error distributions, unbalanced phenotype allocation, outliers, missing values, and dimension reduction, classifier performance (least to greatest error) was ranked as follows: SVM, Random Forest, Naïve Bayes, sPLS-DA, Neural Networks, PLS-DA and k -NN classifiers. When non-normal error distributions were introduced, the performance of PLS-DA and k -NN classifiers deteriorated further relative to the remaining techniques. Over the real datasets, a trend of better performance of SVM and Random Forest classifier performance was observed.

Measuring healthcare integration: Operationalization of a framework for a systems evaluation of palliative care structures, processes, and outcomes.

PubMed

Bainbridge, Daryl; Brazil, Kevin; Ploeg, Jenny; Krueger, Paul; Taniguchi, Alan

2016-06-01

Healthcare integration is a priority in many countries, yet there remains little direction on how to systematically evaluate this construct to inform further development. The examination of community-based palliative care networks provides an ideal opportunity for the advancement of integration measures, in consideration of how fundamental provider cohesion is to effective care at end of life. This article presents a variable-oriented analysis from a theory-based case study of a palliative care network to help bridge the knowledge gap in integration measurement. Data from a mixed-methods case study were mapped to a conceptual framework for evaluating integrated palliative care and a visual array depicting the extent of key factors in the represented palliative care network was formulated. The study included data from 21 palliative care network administrators, 86 healthcare professionals, and 111 family caregivers, all from an established palliative care network in Ontario, Canada. The framework used to guide this research proved useful in assessing qualities of integration and functioning in the palliative care network. The resulting visual array of elements illustrates that while this network performed relatively well at the multiple levels considered, room for improvement exists, particularly in terms of interventions that could facilitate the sharing of information. This study, along with the other evaluative examples mentioned, represents important initial attempts at empirically and comprehensively examining network-integrated palliative care and healthcare integration in general. © The Author(s) 2016.

Arra: Tas::89 0227::Tas Recovery Act 100g Ftp: An Ultra-High Speed Data Transfer Service Over Next Generation 100 Gigabit Per Second Network

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

YU, DANTONG; Jin, Shudong

2014-03-01

Data-intensive applications, including high energy and nuclear physics, astrophysics, climate modeling, nano-scale materials science, genomics, and financing, are expected to generate exabytes of data over the coming years, which must be transferred, visualized, and analyzed by geographically distributed teams of users. High-performance network capabilities must be available to these users at the application level in a transparent, virtualized manner. Moreover, the application users must have the capability to move large datasets from local and remote locations across network environments to their home institutions. To solve these challenges, the main goal of our project is to design and evaluate high-performance datamore » transfer software to support various data-intensive applications. First, we have designed a middleware software that provides access to Remote Direct Memory Access (RDMA) functionalities. This middleware integrates network access, memory management and multitasking in its core design. We address a number of issues related to its efficient implementation, for instance, explicit buffer management and memory registration, and parallelization of RDMA operations, which are vital to delivering the benefit of RDMA to the applications. Built on top of this middleware, an implementation and experimental evaluation of the RDMA-based FTP software, RFTP, is described and evaluated. This application has been implemented by our team to exploit the full capabilities of advanced RDMA mechanisms for ultra-high speed bulk data transfer applications on Energy Sciences Network (ESnet). Second, we designed our data transfer software to optimize TCP/IP based data transfer performance such that RFTP can be fully compatible with today’s Internet. Our kernel optimization techniques with Linux system calls sendfile and splice, can reduce data copy cost. In this report, we summarize the technical challenges of our project, the primary software design methods, the major project milestones achieved, as well as the testbed evaluation work and demonstrations during our project life time.« less

Inferring Broad Regulatory Biology from Time Course Data: Have We Reached an Upper Bound under Constraints Typical of In Vivo Studies?

PubMed Central

Craddock, Travis J. A.; Fletcher, Mary Ann; Klimas, Nancy G.

2015-01-01

There is a growing appreciation for the network biology that regulates the coordinated expression of molecular and cellular markers however questions persist regarding the identifiability of these networks. Here we explore some of the issues relevant to recovering directed regulatory networks from time course data collected under experimental constraints typical of in vivo studies. NetSim simulations of sparsely connected biological networks were used to evaluate two simple feature selection techniques used in the construction of linear Ordinary Differential Equation (ODE) models, namely truncation of terms versus latent vector projection. Performance was compared with ODE-based Time Series Network Identification (TSNI) integral, and the information-theoretic Time-Delay ARACNE (TD-ARACNE). Projection-based techniques and TSNI integral outperformed truncation-based selection and TD-ARACNE on aggregate networks with edge densities of 10-30%, i.e. transcription factor, protein-protein cliques and immune signaling networks. All were more robust to noise than truncation-based feature selection. Performance was comparable on the in silico 10-node DREAM 3 network, a 5-node Yeast synthetic network designed for In vivo Reverse-engineering and Modeling Assessment (IRMA) and a 9-node human HeLa cell cycle network of similar size and edge density. Performance was more sensitive to the number of time courses than to sample frequency and extrapolated better to larger networks by grouping experiments. In all cases performance declined rapidly in larger networks with lower edge density. Limited recovery and high false positive rates obtained overall bring into question our ability to generate informative time course data rather than the design of any particular reverse engineering algorithm. PMID:25984725

The Changing Functions of Citation: From Knowledge Networking to Academic Cash-Value

ERIC Educational Resources Information Center

Burbules, Nicholas C.

2015-01-01

This essay reviews the changing functions, and effects, of citation systems in scholarly research as they move from a range of uses primarily oriented around knowledge networking and epistemic validation, to their use as a set of metrics oriented around evaluating and rewarding certain kinds of academic performance (e.g. "impact…

Design of neural network model-based controller in a fed-batch microbial electrolysis cell reactor for bio-hydrogen gas production

NASA Astrophysics Data System (ADS)

Azwar; Hussain, M. A.; Abdul-Wahab, A. K.; Zanil, M. F.; Mukhlishien

2018-03-01

One of major challenge in bio-hydrogen production process by using MEC process is nonlinear and highly complex system. This is mainly due to the presence of microbial interactions and highly complex phenomena in the system. Its complexity makes MEC system difficult to operate and control under optimal conditions. Thus, precise control is required for the MEC reactor, so that the amount of current required to produce hydrogen gas can be controlled according to the composition of the substrate in the reactor. In this work, two schemes for controlling the current and voltage of MEC were evaluated. The controllers evaluated are PID and Inverse neural network (NN) controller. The comparative study has been carried out under optimal condition for the production of bio-hydrogen gas wherein the controller output is based on the correlation of optimal current and voltage to the MEC. Various simulation tests involving multiple set-point changes and disturbances rejection have been evaluated and the performances of both controllers are discussed. The neural network-based controller results in fast response time and less overshoots while the offset effects are minimal. In conclusion, the Inverse neural network (NN)-based controllers provide better control performance for the MEC system compared to the PID controller.

Evaluation of H.264/AVC over IEEE 802.11p vehicular networks

NASA Astrophysics Data System (ADS)

Rozas-Ramallal, Ismael; Fernández-Caramés, Tiago M.; Dapena, Adriana; García-Naya, José Antonio

2013-12-01

The capacity of vehicular networks to offer non-safety services, like infotainment applications or the exchange of multimedia information between vehicles, have attracted a great deal of attention to the field of Intelligent Transport Systems (ITS). In particular, in this article we focus our attention on IEEE 802.11p which defines enhancements to IEEE 802.11 required to support ITS applications. We present an FPGA-based testbed developed to evaluate H.264/AVC (Advanced Video Coding) video transmission over vehicular networks. The testbed covers some of the most common situations in vehicle-to-vehicle and roadside-to-vehicle communications and it is highly flexible, allowing the performance evaluation of different vehicular standard configurations. We also show several experimental results to illustrate the quality obtained when H.264/AVC encoded video is transmitted over IEEE 802.11p networks. The quality is measured considering two important parameters: the percentage of recovered group of pictures and the frame quality. In order to improve performance, we propose to substitute the convolutional channel encoder used in IEEE 802.11p for a low-density parity-check code encoder. In addition, we suggest a simple strategy to decide the optimum number of iterations needed to decode each packet received.

Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models.

PubMed

Šiljić Tomić, Aleksandra N; Antanasijević, Davor Z; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A; Pocajt, Viktor V

2016-05-01

This paper describes the application of artificial neural network models for the prediction of biological oxygen demand (BOD) levels in the Danube River. Eighteen regularly monitored water quality parameters at 17 stations on the river stretch passing through Serbia were used as input variables. The optimization of the model was performed in three consecutive steps: firstly, the spatial influence of a monitoring station was examined; secondly, the monitoring period necessary to reach satisfactory performance was determined; and lastly, correlation analysis was applied to evaluate the relationship among water quality parameters. Root-mean-square error (RMSE) was used to evaluate model performance in the first two steps, whereas in the last step, multiple statistical indicators of performance were utilized. As a result, two optimized models were developed, a general regression neural network model (labeled GRNN-1) that covers the monitoring stations from the Danube inflow to the city of Novi Sad and a GRNN model (labeled GRNN-2) that covers the stations from the city of Novi Sad to the border with Romania. Both models demonstrated good agreement between the predicted and actually observed BOD values.

Performance evaluation of multi-channel wireless mesh networks with embedded systems.

PubMed

Lam, Jun Huy; Lee, Sang-Gon; Tan, Whye Kit

2012-01-01

Many commercial wireless mesh network (WMN) products are available in the marketplace with their own proprietary standards, but interoperability among the different vendors is not possible. Open source communities have their own WMN implementation in accordance with the IEEE 802.11s draft standard, Linux open80211s project and FreeBSD WMN implementation. While some studies have focused on the test bed of WMNs based on the open80211s project, none are based on the FreeBSD. In this paper, we built an embedded system using the FreeBSD WMN implementation that utilizes two channels and evaluated its performance. This implementation allows the legacy system to connect to the WMN independent of the type of platform and distributes the load between the two non-overlapping channels. One channel is used for the backhaul connection and the other one is used to connect to the stations to wireless mesh network. By using the power efficient 802.11 technology, this device can also be used as a gateway for the wireless sensor network (WSN).

State of the art survey of network operating systems development

NASA Technical Reports Server (NTRS)

1985-01-01

The results of the State-of-the-Art Survey of Network Operating Systems (NOS) performed for Goddard Space Flight Center are presented. NOS functional characteristics are presented in terms of user communication data migration, job migration, network control, and common functional categories. Products (current or future) as well as research and prototyping efforts are summarized. The NOS products which are revelant to the space station and its activities are evaluated.

A Hybrid, Large-Scale Wireless Sensor Network for Real-Time Acquisition and Tracking

DTIC Science & Technology

2007-06-01

multicolor, Quantum Well Infrared Photodetector ( QWIP ), step-stare, large-format Focal Plane Array (FPA) is proposed and evaluated through performance...Photodetector ( QWIP ), step-stare, large-format Focal Plane Array (FPA) is proposed and evaluated through performance analysis. The thesis proposes...7 1. Multi-color IR Sensors - Operational Advantages ...........................8 2. Quantum-Well IR Photodetector ( QWIP

An Evaluation of TCP with Larger Initial Windows

NASA Technical Reports Server (NTRS)

Allman, Mark; Hayes, Christopher; Ostermann, Shawn

1998-01-01

Transmission Control Protocol (TCP's) slow start algorithm gradually increases the amount of data a sender injects into the network, which prevents the sender from overwhelming the network with an inappropriately large burst of traffic. However, the slow start algorithm can make poor use of the available band-width for transfers which are small compared to the bandwidth-delay product of the link, such as file transfers up to few thousand characters over satellite links or even transfers of several hundred bytes over local area networks. This paper evaluates a proposed performance enhancement that raises the initial window used by TCP from 1 MSS-sized segment to roughly 4 KB. The paper evaluates the impact of using larger initial windows on TCP transfers over both the shared Internet and dialup modem links.

Building a Simulation Toolkit for Wireless Mesh Clusters and Evaluating the Suitability of Different Families of Ad Hoc Protocols for the Tactical Network Topology

DTIC Science & Technology

2005-03-01

International Conference On Computers Communications and Networks, 153- 161, Lafayette, L.A. Deitel , H.M. and P.J. Deitel . 2003. C++ How to Program ...of this study is to provide an additional performance evaluation technique for the TNT program of Naval Postgraduate School. The current approach...case are the PAMAS and DBTMA protocols. Toh (2002) illustrates how these approaches succeed in solving the problem. In order to address all the

An Efficient Framework Model for Optimizing Routing Performance in VANETs.

PubMed

Al-Kharasani, Nori M; Zulkarnain, Zuriati Ahmad; Subramaniam, Shamala; Hanapi, Zurina Mohd

2018-02-15

Routing in Vehicular Ad hoc Networks (VANET) is a bit complicated because of the nature of the high dynamic mobility. The efficiency of routing protocol is influenced by a number of factors such as network density, bandwidth constraints, traffic load, and mobility patterns resulting in frequency changes in network topology. Therefore, Quality of Service (QoS) is strongly needed to enhance the capability of the routing protocol and improve the overall network performance. In this paper, we introduce a statistical framework model to address the problem of optimizing routing configuration parameters in Vehicle-to-Vehicle (V2V) communication. Our framework solution is based on the utilization of the network resources to further reflect the current state of the network and to balance the trade-off between frequent changes in network topology and the QoS requirements. It consists of three stages: simulation network stage used to execute different urban scenarios, the function stage used as a competitive approach to aggregate the weighted cost of the factors in a single value, and optimization stage used to evaluate the communication cost and to obtain the optimal configuration based on the competitive cost. The simulation results show significant performance improvement in terms of the Packet Delivery Ratio (PDR), Normalized Routing Load (NRL), Packet loss (PL), and End-to-End Delay (E2ED).

Performance assessments of Android-powered military applications operating on tactical handheld devices

NASA Astrophysics Data System (ADS)

Weiss, Brian A.; Fronczek, Lisa; Morse, Emile; Kootbally, Zeid; Schlenoff, Craig

2013-05-01

Transformative Apps (TransApps) is a Defense Advanced Research Projects Agency (DARPA) funded program whose goal is to develop a range of militarily-relevant software applications ("apps") to enhance the operational-effectiveness of military personnel on (and off) the battlefield. TransApps is also developing a military apps marketplace to facilitate rapid development and dissemination of applications to address user needs by connecting engaged communities of endusers with development groups. The National Institute of Standards and Technology's (NIST) role in the TransApps program is to design and implement evaluation procedures to assess the performance of: 1) the various software applications, 2) software-hardware interactions, and 3) the supporting online application marketplace. Specifically, NIST is responsible for evaluating 50+ tactically-relevant applications operating on numerous Android™-powered platforms. NIST efforts include functional regression testing and quantitative performance testing. This paper discusses the evaluation methodologies employed to assess the performance of three key program elements: 1) handheld-based applications and their integration with various hardware platforms, 2) client-based applications and 3) network technologies operating on both the handheld and client systems along with their integration into the application marketplace. Handheld-based applications are assessed using a combination of utility and usability-based checklists and quantitative performance tests. Client-based applications are assessed to replicate current overseas disconnected (i.e. no network connectivity between handhelds) operations and to assess connected operations envisioned for later use. Finally, networked applications are assessed on handhelds to establish baselines of performance for when connectivity will be common usage.

CCSDS Advanced Orbiting Systems Virtual Channel Access Service for QoS MACHETE Model

NASA Technical Reports Server (NTRS)

Jennings, Esther H.; Segui, John S.

2011-01-01

To support various communications requirements imposed by different missions, interplanetary communication protocols need to be designed, validated, and evaluated carefully. Multimission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE), described in "Simulator of Space Communication Networks" (NPO-41373), NASA Tech Briefs, Vol. 29, No. 8 (August 2005), p. 44, combines various tools for simulation and performance analysis of space networks. The MACHETE environment supports orbital analysis, link budget analysis, communications network simulations, and hardware-in-the-loop testing. By building abstract behavioral models of network protocols, one can validate performance after identifying the appropriate metrics of interest. The innovators have extended the MACHETE model library to include a generic link-layer Virtual Channel (VC) model supporting quality-of-service (QoS) controls based on IP streams. The main purpose of this generic Virtual Channel model addition was to interface fine-grain flow-based QoS (quality of service) between the network and MAC layers of the QualNet simulator, a commercial component of MACHETE. This software model adds the capability of mapping IP streams, based on header fields, to virtual channel numbers, allowing extended QoS handling at link layer. This feature further refines the QoS v existing at the network layer. QoS at the network layer (e.g. diffserv) supports few QoS classes, so data from one class will be aggregated together; differentiating between flows internal to a class/priority is not supported. By adding QoS classification capability between network and MAC layers through VC, one maps multiple VCs onto the same physical link. Users then specify different VC weights, and different queuing and scheduling policies at the link layer. This VC model supports system performance analysis of various virtual channel link-layer QoS queuing schemes independent of the network-layer QoS systems.

Computer Aided Evaluation of Higher Education Tutors' Performance

ERIC Educational Resources Information Center

Xenos, Michalis; Papadopoulos, Thanos

2007-01-01

This article presents a method for computer-aided tutor evaluation: Bayesian Networks are used for organizing the collected data about tutors and for enabling accurate estimations and predictions about future tutor behavior. The model provides indications about each tutor's strengths and weaknesses, which enables the evaluator to exploit strengths…

Performance Comparison of Wireless Sensor Network Standard Protocols in an Aerospace Environment: ISA100.11a and ZigBee

NASA Technical Reports Server (NTRS)

Wagner, Raymond S.; Barton, Richard J.

2011-01-01

Wireless Sensor Networks (WSNs) can provide a substantial benefit in spacecraft systems, reducing launch weight and providing unprecedented flexibility by allowing instrumentation capabilities to grow and change over time. Achieving data transport reliability on par with that of wired systems, however, can prove extremely challenging in practice. Fortunately, much progress has been made in developing standard WSN radio protocols for applications from non-critical home automation to mission-critical industrial process control. The relative performances of candidate protocols must be compared in representative aerospace environments, however, to determine their suitability for spaceflight applications. In this paper, we will present the results of a rigorous laboratory analysis of the performance of two standards-based, low power, low data rate WSN protocols: ZigBee Pro and ISA100.11a. Both are based on IEEE 802.15.4 and augment that standard's specifications to build complete, multi-hop networking stacks. ZigBee Pro targets primarily the home and office automation markets, providing an ad-hoc protocol that is computationally lightweight and easy to implement in inexpensive system-on-a-chip components. As a result of this simplicity, however, ZigBee Pro can be susceptible to radio frequency (RF) interference. ISA100.11a, on the other hand, targets the industrial process control market, providing a robust, centrally-managed protocol capable of tolerating a significant amount of RF interference. To achieve these gains, a coordinated channel hopping mechanism is employed, which entails a greater computational complexity than ZigBee and requires more sophisticated and costly hardware. To guide future aerospace deployments, we must understand how well these standards relatively perform in analog environments under expected operating conditions. Specifically, we are interested in evaluating goodput -- application level throughput -- in a representative crewed environment in the presence of varying levels of 802.11g Wi-Fi traffic. To do so, we use the NASA Johnson Space Center Wireless Habitat Testbed (WHT), a metallic, habitation-sized module designed for co-existence testing of wireless systems. In its quiescent state, the sealed WHT provides an RF-quiet environment to which we can selectively add interfering systems; it also provides a realistic level of multi-path self-interference for systems under investigation. In our test, we deploy two representative five node networks, configured in a star topology with all nodes reporting directly to a WSN gateway. Each ZigBee network WSN node is built using a Texas Instruments (TI) CC2530 system-on-a-chip radio running TI's ZigBee Pro Z-stack. Each ISA100.11a network node is built using a Nivis VersaNode 210 system-on-a-chip radio. In both cases, radios interface with TI MSP430-F5438 microcontroller implementing a common test application. Interference is provided by a D-link 802.11g Wi-Fi router transporting traffic generated using the Iperf network testing tool. For the single-channel ZigBee network, effects of both direct and indirect Wi-Fi interference are evaluated. For the channel-hopping ISA100.11a network, effects of interference from multiple Wi-Fi routers configured in non-overlapping 802.11g channels are evaluated. Our results show that, in general, the more lightweight ZigBee network performs well at low interference levels, but performance degrades as interference increases. Conversely, the more complex and costly ISA100.11a network continues to perform well as Wi-Fi interference levels increase.

Simulator of Space Communication Networks

NASA Technical Reports Server (NTRS)

Clare, Loren; Jennings, Esther; Gao, Jay; Segui, John; Kwong, Winston

2005-01-01

Multimission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) is a suite of software tools that simulates the behaviors of communication networks to be used in space exploration, and predict the performance of established and emerging space communication protocols and services. MACHETE consists of four general software systems: (1) a system for kinematic modeling of planetary and spacecraft motions; (2) a system for characterizing the engineering impact on the bandwidth and reliability of deep-space and in-situ communication links; (3) a system for generating traffic loads and modeling of protocol behaviors and state machines; and (4) a system of user-interface for performance metric visualizations. The kinematic-modeling system makes it possible to characterize space link connectivity effects, including occultations and signal losses arising from dynamic slant-range changes and antenna radiation patterns. The link-engineering system also accounts for antenna radiation patterns and other phenomena, including modulations, data rates, coding, noise, and multipath fading. The protocol system utilizes information from the kinematic-modeling and link-engineering systems to simulate operational scenarios of space missions and evaluate overall network performance. In addition, a Communications Effect Server (CES) interface for MACHETE has been developed to facilitate hybrid simulation of space communication networks with actual flight/ground software/hardware embedded in the overall system.

Evaluation of the annual Canadian biodosimetry network intercomparisons

PubMed Central

Wilkins, Ruth C.; Beaton-Green, Lindsay A.; Lachapelle, Sylvie; Kutzner, Barbara C.; Ferrarotto, Catherine; Chauhan, Vinita; Marro, Leonora; Livingston, Gordon K.; Boulay Greene, Hillary; Flegal, Farrah N.

2015-01-01

Abstract Purpose: To evaluate the importance of annual intercomparisons for maintaining the capacity and capabilities of a well-established biodosimetry network in conjunction with assessing efficient and effective analysis methods for emergency response. Materials and methods: Annual intercomparisons were conducted between laboratories in the Canadian National Biological Dosimetry Response Plan. Intercomparisons were performed over a six-year period and comprised of the shipment of 10–12 irradiated, blinded blood samples for analysis by each of the participating laboratories. Dose estimates were determined by each laboratory using the dicentric chromosome assay (conventional and QuickScan scoring) and where possible the cytokinesis block micronucleus (CBMN) assay. Dose estimates were returned to the lead laboratory for evaluation and comparison. Results: Individual laboratories performed comparably from year to year with only slight fluctuations in performance. Dose estimates using the dicentric chromosome assay were accurate about 80% of the time and the QuickScan method for scoring the dicentric chromosome assay was proven to reduce the time of analysis without having a significant effect on the dose estimates. Although analysis with the CBMN assay was comparable to QuickScan scoring with respect to speed, the accuracy of the dose estimates was greatly reduced. Conclusions: Annual intercomparisons are necessary to maintain a network of laboratories for emergency response biodosimetry as they evoke confidence in their capabilities. PMID:25670072

Cerebral networks of sustained attention and working memory: a functional magnetic resonance imaging study based on the Continuous Performance Test.

PubMed

Bartés-Serrallonga, M; Adan, A; Solé-Casals, J; Caldú, X; Falcón, C; Pérez-Pàmies, M; Bargalló, N; Serra-Grabulosa, J M

2014-04-01

One of the most used paradigms in the study of attention is the Continuous Performance Test (CPT). The identical pairs version (CPT-IP) has been widely used to evaluate attention deficits in developmental, neurological and psychiatric disorders. However, the specific locations and the relative distribution of brain activation in networks identified with functional imaging, varies significantly with differences in task design. To design a task to evaluate sustained attention using functional magnetic resonance imaging (fMRI), and thus to provide data for research concerned with the role of these functions. Forty right-handed, healthy students (50% women; age range: 18-25 years) were recruited. A CPT-IP implemented as a block design was used to assess sustained attention during the fMRI session. The behavioural results from the CPT-IP task showed a good performance in all subjects, higher than 80% of hits. fMRI results showed that the used CPT-IP task activates a network of frontal, parietal and occipital areas, and that these are related to executive and attentional functions. In relation to the use of the CPT to study of attention and working memory, this task provides normative data in healthy adults, and it could be useful to evaluate disorders which have attentional and working memory deficits.

Evaluation of multicast schemes in optical burst-switched networks: the case with dynamic sessions

NASA Astrophysics Data System (ADS)

Jeong, Myoungki; Qiao, Chunming; Xiong, Yijun; Vandenhoute, Marc

2000-10-01

In this paper, we evaluate the performance of several multicast schemes in optical burst-switched WDM networks taking into accounts the overheads due to control packets and guard bands (Gbs) of bursts on separate channels (wavelengths). A straightforward scheme is called Separate Multicasting (S-MCAST) where each source node constructs separate bursts for its multicast (per each multicast session) and unicast traffic. To reduce the overhead due to Gbs (and control packets), one may piggyback the multicast traffic in bursts containing unicast traffic using a scheme called Multiple Unicasting (M-UCAST). The third scheme is called Tree-Shared Multicasting (TS-MCAST) wehreby multicast traffic belonging to multiple multicast sesions can be mixed together in a burst, which is delivered via a shared multicast tree. In [1], we have evaluated several multicast schemes with static sessions at the flow level. In this paper, we perform a simple analysis for the multicast schemes and evaluate the performance of three multicast schemes, focusing on the case with dynamic sessions in terms of the link utilization, bandwidth consumption, blocking (loss) probability, goodput and the processing loads.

Relations between mental health team characteristics and work role performance.

PubMed

Fleury, Marie-Josée; Grenier, Guy; Bamvita, Jean-Marie; Farand, Lambert

2017-01-01

Effective mental health care requires a high performing, interprofessional team. Among 79 mental health teams in Quebec (Canada), this exploratory study aims to 1) determine the association between work role performance and a wide range of variables related to team effectiveness according to the literature, and to 2) using structural equation modelling, assess the covariance between each of these variables as well as the correlation with other exogenous variables. Work role performance was measured with an adapted version of a work role questionnaire. Various independent variables including team manager characteristics, user characteristics, team profiles, clinical activities, organizational culture, network integration strategies and frequency/satisfaction of interactions with other teams or services were analyzed under the structural equation model. The later provided a good fit with the data. Frequent use of standardized procedures and evaluation tools (e.g. screening and assessment tools for mental health disorders) and team manager seniority exerted the most direct effect on work role performance. While network integration strategies had little effect on work role performance, there was a high covariance between this variable and those directly affecting work role performance among mental health teams. The results suggest that the mental healthcare system should apply standardized procedures and evaluation tools and, to a lesser extent, clinical approaches to improve work role performance in mental health teams. Overall, a more systematic implementation of network integration strategies may contribute to improved work role performance in mental health care.

Relations between mental health team characteristics and work role performance

PubMed Central

Grenier, Guy; Bamvita, Jean-Marie; Farand, Lambert

2017-01-01

Effective mental health care requires a high performing, interprofessional team. Among 79 mental health teams in Quebec (Canada), this exploratory study aims to 1) determine the association between work role performance and a wide range of variables related to team effectiveness according to the literature, and to 2) using structural equation modelling, assess the covariance between each of these variables as well as the correlation with other exogenous variables. Work role performance was measured with an adapted version of a work role questionnaire. Various independent variables including team manager characteristics, user characteristics, team profiles, clinical activities, organizational culture, network integration strategies and frequency/satisfaction of interactions with other teams or services were analyzed under the structural equation model. The later provided a good fit with the data. Frequent use of standardized procedures and evaluation tools (e.g. screening and assessment tools for mental health disorders) and team manager seniority exerted the most direct effect on work role performance. While network integration strategies had little effect on work role performance, there was a high covariance between this variable and those directly affecting work role performance among mental health teams. The results suggest that the mental healthcare system should apply standardized procedures and evaluation tools and, to a lesser extent, clinical approaches to improve work role performance in mental health teams. Overall, a more systematic implementation of network integration strategies may contribute to improved work role performance in mental health care. PMID:28991923

Performance analysis of multi-radio routing protocol in cognitive radio ad hoc networks under different path failure rate

NASA Astrophysics Data System (ADS)

Che-Aron, Z.; Abdalla, A. H.; Abdullah, K.; Hassan, W. H.

2013-12-01

In recent years, Cognitive Radio (CR) technology has largely attracted significant studies and research. Cognitive Radio Ad Hoc Network (CRAHN) is an emerging self-organized, multi-hop, wireless network which allows unlicensed users to opportunistically access available licensed spectrum bands for data communication under an intelligent and cautious manner. However, in CRAHNs, a lot of failures can easily occur during data transmission caused by PU (Primary User) activity, topology change, node fault, or link degradation. In this paper, an attempt has been made to evaluate the performance of the Multi-Radio Link-Quality Source Routing (MR-LQSR) protocol in CRAHNs under different path failure rate. In the MR-LQSR protocol, the Weighted Cumulative Expected Transmission Time (WCETT) is used as the routing metric. The simulations are carried out using the NS-2 simulator. The protocol performance is evaluated with respect to performance metrics like average throughput, packet loss, average end-to-end delay and average jitter. From the simulation results, it is observed that the number of path failures depends on the PUs number and mobility rate of SUs (Secondary Users). Moreover, the protocol performance is greatly affected when the path failure rate is high, leading to major service outages.

An innovative and comprehensive technique to evaluate different measures of medication adherence: The network meta-analysis.

PubMed

Tonin, Fernanda S; Wiecek, Elyssa; Torres-Robles, Andrea; Pontarolo, Roberto; Benrimoj, Shalom Charlie I; Fernandez-Llimos, Fernando; Garcia-Cardenas, Victoria

2018-05-19

Poor medication adherence is associated with adverse health outcomes and higher costs of care. However, inconsistencies in the assessment of adherence are found in the literature. To evaluate the effect of different measures of adherence in the comparative effectiveness of complex interventions to enhance patients' adherence to prescribed medications. A systematic review with network meta-analysis was performed. Electronic searches for relevant pairwise meta-analysis including trials of interventions that aimed to improve medication adherence were performed in PubMed. Data extraction was conducted with eligible trials evaluating short-period adherence follow-up (until 3 months) using any measure of adherence: self-report, pill count, or MEMS (medication event monitoring system). To standardize the results obtained with these different measures, an overall composite measure and an objective composite measure were also calculated. Network meta-analyses for each measure of adherence were built. Rank order and surface under the cumulative ranking curve analyses (SUCRA) were performed. Ninety-one trials were included in the network meta-analyses. The five network meta-analyses demonstrated robustness and reliability. Results obtained for all measures of adherence were similar across them and to both composite measures. For both composite measures, interventions comprising economic + technical components were the best option (90% of probability in SUCRA analysis) with statistical superiority against almost all other interventions and against standard care (odds ratio with 95% credibility interval ranging from 0.09 to 0.25 [0.02, 0.98]). The use of network meta-analysis was reliable to compare different measures of adherence of complex interventions in short-periods follow-up. Analyses with longer follow-up periods are needed to confirm these results. Different measures of adherence produced similar results. The use of composite measures revealed reliable alternatives to establish a broader and more detailed picture of adherence. Copyright © 2018 Elsevier Inc. All rights reserved.

Data Driven Performance Evaluation of Wireless Sensor Networks

PubMed Central

Frery, Alejandro C.; Ramos, Heitor S.; Alencar-Neto, José; Nakamura, Eduardo; Loureiro, Antonio A. F.

2010-01-01

Wireless Sensor Networks are presented as devices for signal sampling and reconstruction. Within this framework, the qualitative and quantitative influence of (i) signal granularity, (ii) spatial distribution of sensors, (iii) sensors clustering, and (iv) signal reconstruction procedure are assessed. This is done by defining an error metric and performing a Monte Carlo experiment. It is shown that all these factors have significant impact on the quality of the reconstructed signal. The extent of such impact is quantitatively assessed. PMID:22294920

An Adaptive QoS Routing Solution for MANET Based Multimedia Communications in Emergency Cases

NASA Astrophysics Data System (ADS)

Ramrekha, Tipu Arvind; Politis, Christos

The Mobile Ad hoc Networks (MANET) is a wireless network deprived of any fixed central authoritative routing entity. It relies entirely on collaborating nodes forwarding packets from source to destination. This paper describes the design, implementation and performance evaluation of CHAMELEON, an adaptive Quality of Service (QoS) routing solution, with improved delay and jitter performances, enabling multimedia communication for MANETs in extreme emergency situations such as forest fire and terrorist attacks as defined in the PEACE project. CHAMELEON is designed to adapt its routing behaviour according to the size of a MANET. The reactive Ad Hoc on-Demand Distance Vector Routing (AODV) and proactive Optimized Link State Routing (OLSR) protocols are deemed appropriate for CHAMELEON through their performance evaluation in terms of delay and jitter for different MANET sizes in a building fire emergency scenario. CHAMELEON is then implemented in NS-2 and evaluated similarly. The paper concludes with a summary of findings so far and intended future work.

Core networks and their reconfiguration patterns across cognitive loads.

PubMed

Zuo, Nianming; Yang, Zhengyi; Liu, Yong; Li, Jin; Jiang, Tianzi

2018-04-20

Different cognitively demanding tasks recruit globally distributed but functionally specific networks. However, the configuration of core networks and their reconfiguration patterns across cognitive loads remain unclear, as does whether these patterns are indicators for the performance of cognitive tasks. In this study, we analyzed functional magnetic resonance imaging data of a large cohort of 448 subjects, acquired with the brain at resting state and executing N-back working memory (WM) tasks. We discriminated core networks by functional interaction strength and connection flexibility. Results demonstrated that the frontoparietal network (FPN) and default mode network (DMN) were core networks, but each exhibited different patterns across cognitive loads. The FPN and DMN both showed strengthened internal connections at the low demand state (0-back) compared with the resting state (control level); whereas, from the low (0-back) to high demand state (2-back), some connections to the FPN weakened and were rewired to the DMN (whose connections all remained strong). Of note, more intensive reconfiguration of both the whole brain and core networks (but no other networks) across load levels indicated relatively poor cognitive performance. Collectively these findings indicate that the FPN and DMN have distinct roles and reconfiguration patterns across cognitively demanding loads. This study advances our understanding of the core networks and their reconfiguration patterns across cognitive loads and provides a new feature to evaluate and predict cognitive capability (e.g., WM performance) based on brain networks. © 2018 Wiley Periodicals, Inc.

An improved spanning tree approach for the reliability analysis of supply chain collaborative network

NASA Astrophysics Data System (ADS)

Lam, C. Y.; Ip, W. H.

2012-11-01

A higher degree of reliability in the collaborative network can increase the competitiveness and performance of an entire supply chain. As supply chain networks grow more complex, the consequences of unreliable behaviour become increasingly severe in terms of cost, effort and time. Moreover, it is computationally difficult to calculate the network reliability of a Non-deterministic Polynomial-time hard (NP-hard) all-terminal network using state enumeration, as this may require a huge number of iterations for topology optimisation. Therefore, this paper proposes an alternative approach of an improved spanning tree for reliability analysis to help effectively evaluate and analyse the reliability of collaborative networks in supply chains and reduce the comparative computational complexity of algorithms. Set theory is employed to evaluate and model the all-terminal reliability of the improved spanning tree algorithm and present a case study of a supply chain used in lamp production to illustrate the application of the proposed approach.

Plant Species Identification by Bi-channel Deep Convolutional Networks

NASA Astrophysics Data System (ADS)

He, Guiqing; Xia, Zhaoqiang; Zhang, Qiqi; Zhang, Haixi; Fan, Jianping

2018-04-01

Plant species identification achieves much attention recently as it has potential application in the environmental protection and human life. Although deep learning techniques can be directly applied for plant species identification, it still needs to be designed for this specific task to obtain the state-of-art performance. In this paper, a bi-channel deep learning framework is developed for identifying plant species. In the framework, two different sub-networks are fine-tuned over their pretrained models respectively. And then a stacking layer is used to fuse the output of two different sub-networks. We construct a plant dataset of Orchidaceae family for algorithm evaluation. Our experimental results have demonstrated that our bi-channel deep network can achieve very competitive performance on accuracy rates compared to the existing deep learning algorithm.

A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2001-01-01

In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

Marine vessels as substitutes for heavy-duty trucks in Great Lakes freight transportation.

PubMed

Comer, Bryan; Corbett, James J; Hawker, J Scott; Korfmacher, Karl; Lee, Earl E; Prokop, Chris; Winebrake, James J

2010-07-01

This paper applies a geospatial network optimization model to explore environmental, economic, and time-of-delivery tradeoffs associated with the application of marine vessels as substitutes for heavy-duty trucks operating in the Great Lakes region. The geospatial model integrates U.S. and Canadian highway, rail, and waterway networks to create an intermodal network and characterizes this network using temporal, economic, and environmental attributes (including emissions of carbon dioxide, particulate matter, carbon monoxide, sulfur oxides, volatile organic compounds, and nitrogen oxides). A case study evaluates tradeoffs associated with containerized traffic flow in the Great Lakes region, demonstrating how choice of freight mode affects the environmental performance of movement of goods. These results suggest opportunities to improve the environmental performance of freight transport through infrastructure development, technology implementation, and economic incentives.

Portable emergency telemedicine system over wireless broadband and 3G networks.

PubMed

Hong, SungHye; Kim, SangYong; Kim, JungChae; Lim, DongKyu; Jung, SeokMyung; Kim, DongKeun; Yoo, Sun K

2009-01-01

The telemedicine system aims at monitoring patients remotely without limit in time and space. However the existing telemedicine systems exchange medical information simply in a specified location. Due to increasing speed in processing data and expanding bandwidth of wireless networks, it is possible to perform telemedicine services on personal digital assistants (PDA). In this paper, a telemedicine system on PDA was developed using wideband mobile networks such as Wi-Fi, HSDPA, and WiBro for high speed bandwidths. This system enables to utilize and exchange variety and reliable patient information of video, biosignals, chatting messages, and triage data. By measuring bandwidths of individual data of the system over wireless networks, and evaluating the performance of this system using PDA, we demonstrated the feasibility of the designed portable emergency telemedicine system.

Adaptive control of nonlinear system using online error minimum neural networks.

PubMed

Jia, Chao; Li, Xiaoli; Wang, Kang; Ding, Dawei

2016-11-01

In this paper, a new learning algorithm named OEM-ELM (Online Error Minimized-ELM) is proposed based on ELM (Extreme Learning Machine) neural network algorithm and the spreading of its main structure. The core idea of this OEM-ELM algorithm is: online learning, evaluation of network performance, and increasing of the number of hidden nodes. It combines the advantages of OS-ELM and EM-ELM, which can improve the capability of identification and avoid the redundancy of networks. The adaptive control based on the proposed algorithm OEM-ELM is set up which has stronger adaptive capability to the change of environment. The adaptive control of chemical process Continuous Stirred Tank Reactor (CSTR) is also given for application. The simulation results show that the proposed algorithm with respect to the traditional ELM algorithm can avoid network redundancy and improve the control performance greatly. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

NetCoDer: A Retransmission Mechanism for WSNs Based on Cooperative Relays and Network Coding

PubMed Central

Valle, Odilson T.; Montez, Carlos; Medeiros de Araujo, Gustavo; Vasques, Francisco; Moraes, Ricardo

2016-01-01

Some of the most difficult problems to deal with when using Wireless Sensor Networks (WSNs) are related to the unreliable nature of communication channels. In this context, the use of cooperative diversity techniques and the application of network coding concepts may be promising solutions to improve the communication reliability. In this paper, we propose the NetCoDer scheme to address this problem. Its design is based on merging cooperative diversity techniques and network coding concepts. We evaluate the effectiveness of the NetCoDer scheme through both an experimental setup with real WSN nodes and a simulation assessment, comparing NetCoDer performance against state-of-the-art TDMA-based (Time Division Multiple Access) retransmission techniques: BlockACK, Master/Slave and Redundant TDMA. The obtained results highlight that the proposed NetCoDer scheme clearly improves the network performance when compared with other retransmission techniques. PMID:27258280

Detection of pseudosinusoidal epileptic seizure segments in the neonatal EEG by cascading a rule-based algorithm with a neural network.

PubMed

Karayiannis, Nicolaos B; Mukherjee, Amit; Glover, John R; Ktonas, Periklis Y; Frost, James D; Hrachovy, Richard A; Mizrahi, Eli M

2006-04-01

This paper presents an approach to detect epileptic seizure segments in the neonatal electroencephalogram (EEG) by characterizing the spectral features of the EEG waveform using a rule-based algorithm cascaded with a neural network. A rule-based algorithm screens out short segments of pseudosinusoidal EEG patterns as epileptic based on features in the power spectrum. The output of the rule-based algorithm is used to train and compare the performance of conventional feedforward neural networks and quantum neural networks. The results indicate that the trained neural networks, cascaded with the rule-based algorithm, improved the performance of the rule-based algorithm acting by itself. The evaluation of the proposed cascaded scheme for the detection of pseudosinusoidal seizure segments reveals its potential as a building block of the automated seizure detection system under development.

A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization.

PubMed

Tuikkala, Johannes; Vähämaa, Heidi; Salmela, Pekka; Nevalainen, Olli S; Aittokallio, Tero

2012-03-26

Graph drawing is an integral part of many systems biology studies, enabling visual exploration and mining of large-scale biological networks. While a number of layout algorithms are available in popular network analysis platforms, such as Cytoscape, it remains poorly understood how well their solutions reflect the underlying biological processes that give rise to the network connectivity structure. Moreover, visualizations obtained using conventional layout algorithms, such as those based on the force-directed drawing approach, may become uninformative when applied to larger networks with dense or clustered connectivity structure. We implemented a modified layout plug-in, named Multilevel Layout, which applies the conventional layout algorithms within a multilevel optimization framework to better capture the hierarchical modularity of many biological networks. Using a wide variety of real life biological networks, we carried out a systematic evaluation of the method in comparison with other layout algorithms in Cytoscape. The multilevel approach provided both biologically relevant and visually pleasant layout solutions in most network types, hence complementing the layout options available in Cytoscape. In particular, it could improve drawing of large-scale networks of yeast genetic interactions and human physical interactions. In more general terms, the biological evaluation framework developed here enables one to assess the layout solutions from any existing or future graph drawing algorithm as well as to optimize their performance for a given network type or structure. By making use of the multilevel modular organization when visualizing biological networks, together with the biological evaluation of the layout solutions, one can generate convenient visualizations for many network biology applications.

State Space Model with hidden variables for reconstruction of gene regulatory networks.

PubMed

Wu, Xi; Li, Peng; Wang, Nan; Gong, Ping; Perkins, Edward J; Deng, Youping; Zhang, Chaoyang

2011-01-01

State Space Model (SSM) is a relatively new approach to inferring gene regulatory networks. It requires less computational time than Dynamic Bayesian Networks (DBN). There are two types of variables in the linear SSM, observed variables and hidden variables. SSM uses an iterative method, namely Expectation-Maximization, to infer regulatory relationships from microarray datasets. The hidden variables cannot be directly observed from experiments. How to determine the number of hidden variables has a significant impact on the accuracy of network inference. In this study, we used SSM to infer Gene regulatory networks (GRNs) from synthetic time series datasets, investigated Bayesian Information Criterion (BIC) and Principle Component Analysis (PCA) approaches to determining the number of hidden variables in SSM, and evaluated the performance of SSM in comparison with DBN. True GRNs and synthetic gene expression datasets were generated using GeneNetWeaver. Both DBN and linear SSM were used to infer GRNs from the synthetic datasets. The inferred networks were compared with the true networks. Our results show that inference precision varied with the number of hidden variables. For some regulatory networks, the inference precision of DBN was higher but SSM performed better in other cases. Although the overall performance of the two approaches is compatible, SSM is much faster and capable of inferring much larger networks than DBN. This study provides useful information in handling the hidden variables and improving the inference precision.

Improving the Unsteady Aerodynamic Performance of Transonic Turbines using Neural Networks

NASA Technical Reports Server (NTRS)

Rai, Man Mohan; Madavan, Nateri K.; Huber, Frank W.

1999-01-01

A recently developed neural net-based aerodynamic design procedure is used in the redesign of a transonic turbine stage to improve its unsteady aerodynamic performance. The redesign procedure used incorporates the advantages of both traditional response surface methodology and neural networks by employing a strategy called parameter-based partitioning of the design space. Starting from the reference design, a sequence of response surfaces based on both neural networks and polynomial fits are constructed to traverse the design space in search of an optimal solution that exhibits improved unsteady performance. The procedure combines the power of neural networks and the economy of low-order polynomials (in terms of number of simulations required and network training requirements). A time-accurate, two-dimensional, Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the optimization procedure. The procedure yielded a modified design that improves the aerodynamic performance through small changes to the reference design geometry. These results demonstrate the capabilities of the neural net-based design procedure, and also show the advantages of including high-fidelity unsteady simulations that capture the relevant flow physics in the design optimization process.

On Estimating End-to-End Network Path Properties

NASA Technical Reports Server (NTRS)

Allman, Mark; Paxson, Vern

1999-01-01

The more information about current network conditions available to a transport protocol, the more efficiently it can use the network to transfer its data. In networks such as the Internet, the transport protocol must often form its own estimates of network properties based on measurements per-formed by the connection endpoints. We consider two basic transport estimation problems: determining the setting of the retransmission timer (RTO) for are reliable protocol, and estimating the bandwidth available to a connection as it begins. We look at both of these problems in the context of TCP, using a large TCP measurement set [Pax97b] for trace-driven simulations. For RTO estimation, we evaluate a number of different algorithms, finding that the performance of the estimators is dominated by their minimum values, and to a lesser extent, the timer granularity, while being virtually unaffected by how often round-trip time measurements are made or the settings of the parameters in the exponentially-weighted moving average estimators commonly used. For bandwidth estimation, we explore techniques previously sketched in the literature [Hoe96, AD98] and find that in practice they perform less well than anticipated. We then develop a receiver-side algorithm that performs significantly better.

POCO-MOEA: Using Evolutionary Algorithms to Solve the Controller Placement Problem

DTIC Science & Technology

2016-03-24

to gather data on POCO-MOEA performance to a series of iv model networks. The algorithm’s behavior is then evaluated and compared to ex- haustive... evaluation of a third heuristic based on a Multi 3 Objective Evolutionary Algorithm (MOEA). This heuristic is modeled after one of the most well known MOEAs...researchers to extend into more realistic evaluations of the performance characteristics of SDN controllers, such as the use of simulators or live

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.

Spectrum efficient distance-adaptive paths for fixed and fixed-alternate routing in elastic optical networks

NASA Astrophysics Data System (ADS)

Agrawal, Anuj; Bhatia, Vimal; Prakash, Shashi

2018-01-01

Efficient utilization of spectrum is a key concern in the soon to be deployed elastic optical networks (EONs). To perform routing in EONs, various fixed routing (FR), and fixed-alternate routing (FAR) schemes are ubiquitously used. FR, and FAR schemes calculate a fixed route, and a prioritized list of a number of alternate routes, respectively, between different pairs of origin o and target t nodes in the network. The route calculation performed using FR and FAR schemes is predominantly based on either the physical distance, known as k -shortest paths (KSP), or on the hop count (HC). For survivable optical networks, FAR usually calculates link-disjoint (LD) paths. These conventional routing schemes have been efficiently used for decades in communication networks. However, in this paper, it has been demonstrated that these commonly used routing schemes cannot utilize the network spectral resources optimally in the newly introduced EONs. Thus, we propose a new routing scheme for EON, namely, k -distance adaptive paths (KDAP) that efficiently utilizes the benefit of distance-adaptive modulation, and bit rate-adaptive superchannel capability inherited by EON to improve spectrum utilization. In the proposed KDAP, routes are found and prioritized on the basis of bit rate, distance, spectrum granularity, and the number of links used for a particular route. To evaluate the performance of KSP, HC, LD, and the proposed KDAP, simulations have been performed for three different sized networks, namely, 7-node test network (TEST7), NSFNET, and 24-node US backbone network (UBN24). We comprehensively assess the performance of various conventional, and the proposed routing schemes by solving both the RSA and the dual RSA problems under homogeneous and heterogeneous traffic requirements. Simulation results demonstrate that there is a variation amongst the performance of KSP, HC, and LD, depending on the o - t pair, and the network topology and its connectivity. However, the proposed KDAP always performs better for all the considered networks and traffic scenarios, as compared to the conventional routing schemes, namely, KSP, HC, and LD. The proposed KDAP achieves up to 60 % , and 10.46 % improvement in terms of spectrum utilization, and resource utilization ratio, respectively, over the conventional routing schemes.

Using a Simple Neural Network to Delineate Some Principles of Distributed Economic Choice.

PubMed

Balasubramani, Pragathi P; Moreno-Bote, Rubén; Hayden, Benjamin Y

2018-01-01

The brain uses a mixture of distributed and modular organization to perform computations and generate appropriate actions. While the principles under which the brain might perform computations using modular systems have been more amenable to modeling, the principles by which the brain might make choices using distributed principles have not been explored. Our goal in this perspective is to delineate some of those distributed principles using a neural network method and use its results as a lens through which to reconsider some previously published neurophysiological data. To allow for direct comparison with our own data, we trained the neural network to perform binary risky choices. We find that value correlates are ubiquitous and are always accompanied by non-value information, including spatial information (i.e., no pure value signals). Evaluation, comparison, and selection were not distinct processes; indeed, value signals even in the earliest stages contributed directly, albeit weakly, to action selection. There was no place, other than at the level of action selection, at which dimensions were fully integrated. No units were specialized for specific offers; rather, all units encoded the values of both offers in an anti-correlated format, thus contributing to comparison. Individual network layers corresponded to stages in a continuous rotation from input to output space rather than to functionally distinct modules. While our network is likely to not be a direct reflection of brain processes, we propose that these principles should serve as hypotheses to be tested and evaluated for future studies.

Using a Simple Neural Network to Delineate Some Principles of Distributed Economic Choice

PubMed Central

Balasubramani, Pragathi P.; Moreno-Bote, Rubén; Hayden, Benjamin Y.

2018-01-01

The brain uses a mixture of distributed and modular organization to perform computations and generate appropriate actions. While the principles under which the brain might perform computations using modular systems have been more amenable to modeling, the principles by which the brain might make choices using distributed principles have not been explored. Our goal in this perspective is to delineate some of those distributed principles using a neural network method and use its results as a lens through which to reconsider some previously published neurophysiological data. To allow for direct comparison with our own data, we trained the neural network to perform binary risky choices. We find that value correlates are ubiquitous and are always accompanied by non-value information, including spatial information (i.e., no pure value signals). Evaluation, comparison, and selection were not distinct processes; indeed, value signals even in the earliest stages contributed directly, albeit weakly, to action selection. There was no place, other than at the level of action selection, at which dimensions were fully integrated. No units were specialized for specific offers; rather, all units encoded the values of both offers in an anti-correlated format, thus contributing to comparison. Individual network layers corresponded to stages in a continuous rotation from input to output space rather than to functionally distinct modules. While our network is likely to not be a direct reflection of brain processes, we propose that these principles should serve as hypotheses to be tested and evaluated for future studies. PMID:29643773

Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks.

PubMed

Hsieh, Chung-Ho; Lu, Ruey-Hwa; Lee, Nai-Hsin; Chiu, Wen-Ta; Hsu, Min-Huei; Li, Yu-Chuan Jack

2011-01-01

Diagnosing acute appendicitis clinically is still difficult. We developed random forests, support vector machines, and artificial neural network models to diagnose acute appendicitis. Between January 2006 and December 2008, patients who had a consultation session with surgeons for suspected acute appendicitis were enrolled. Seventy-five percent of the data set was used to construct models including random forest, support vector machines, artificial neural networks, and logistic regression. Twenty-five percent of the data set was withheld to evaluate model performance. The area under the receiver operating characteristic curve (AUC) was used to evaluate performance, which was compared with that of the Alvarado score. Data from a total of 180 patients were collected, 135 used for training and 45 for testing. The mean age of patients was 39.4 years (range, 16-85). Final diagnosis revealed 115 patients with and 65 without appendicitis. The AUC of random forest, support vector machines, artificial neural networks, logistic regression, and Alvarado was 0.98, 0.96, 0.91, 0.87, and 0.77, respectively. The sensitivity, specificity, positive, and negative predictive values of random forest were 94%, 100%, 100%, and 87%, respectively. Random forest performed better than artificial neural networks, logistic regression, and Alvarado. We demonstrated that random forest can predict acute appendicitis with good accuracy and, deployed appropriately, can be an effective tool in clinical decision making. Copyright © 2011 Mosby, Inc. All rights reserved.

Test and Evaluation of a Prototyped Sensor-Camera Network for Persistent Intelligence, Surveillance, and Reconnaissance in Support of Tactical Coalition Networking Environments

DTIC Science & Technology

2006-06-01

scenarios. The demonstration planned for May 2006, in Chiang Mai , Thailand, will have a first-responder, law enforcement, and counter-terrorism and counter...to local ( Chiang Mai ), theater (Bangkok), and global (Alameda, California) command and control centers. This fusion of information validates using...network performance to be tested during moderate environmental conditions. The third and fourth scenarios were conducted in Chiang Mai , Thailand

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.

Optical multicast system for data center networks.

PubMed

Samadi, Payman; Gupta, Varun; Xu, Junjie; Wang, Howard; Zussman, Gil; Bergman, Keren

2015-08-24

We present the design and experimental evaluation of an Optical Multicast System for Data Center Networks, a hardware-software system architecture that uniquely integrates passive optical splitters in a hybrid network architecture for faster and simpler delivery of multicast traffic flows. An application-driven control plane manages the integrated optical and electronic switched traffic routing in the data plane layer. The control plane includes a resource allocation algorithm to optimally assign optical splitters to the flows. The hardware architecture is built on a hybrid network with both Electronic Packet Switching (EPS) and Optical Circuit Switching (OCS) networks to aggregate Top-of-Rack switches. The OCS is also the connectivity substrate of splitters to the optical network. The optical multicast system implementation requires only commodity optical components. We built a prototype and developed a simulation environment to evaluate the performance of the system for bulk multicasting. Experimental and numerical results show simultaneous delivery of multicast flows to all receivers with steady throughput. Compared to IP multicast that is the electronic counterpart, optical multicast performs with less protocol complexity and reduced energy consumption. Compared to peer-to-peer multicast methods, it achieves at minimum an order of magnitude higher throughput for flows under 250 MB with significantly less connection overheads. Furthermore, for delivering 20 TB of data containing only 15% multicast flows, it reduces the total delivery energy consumption by 50% and improves latency by 55% compared to a data center with a sole non-blocking EPS network.

Network information improves cancer outcome prediction.

PubMed

Roy, Janine; Winter, Christof; Isik, Zerrin; Schroeder, Michael

2014-07-01

Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. One approach to deal with these two problems employs protein-protein interaction networks and ranks genes using the random surfer model of Google's PageRank algorithm. In this work, we created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and systematically evaluated the use of networks and a PageRank derivative, NetRank, for signature identification. We show that the NetRank performs significantly better than classical methods such as fold change or t-test. Despite an order of magnitude difference in network size, a regulatory and protein-protein interaction network perform equally well. Experimental evaluation on cancer outcome prediction in all of the 25 underlying datasets suggests that the network-based methodology identifies highly overlapping signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. Integration of network information into gene expression analysis allows the identification of more reliable and accurate biomarkers and provides a deeper understanding of processes occurring in cancer development and progression. © The Author 2012. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

A Faster Parallel Algorithm and Efficient Multithreaded Implementations for Evaluating Betweenness Centrality on Massive Datasets

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

Madduri, Kamesh; Ediger, David; Jiang, Karl

2009-02-15

We present a new lock-free parallel algorithm for computing betweenness centralityof massive small-world networks. With minor changes to the data structures, ouralgorithm also achieves better spatial cache locality compared to previous approaches. Betweenness centrality is a key algorithm kernel in HPCS SSCA#2, a benchmark extensively used to evaluate the performance of emerging high-performance computing architectures for graph-theoretic computations. We design optimized implementations of betweenness centrality and the SSCA#2 benchmark for two hardware multithreaded systems: a Cray XMT system with the Threadstorm processor, and a single-socket Sun multicore server with the UltraSPARC T2 processor. For a small-world network of 134 millionmore » vertices and 1.073 billion edges, the 16-processor XMT system and the 8-core Sun Fire T5120 server achieve TEPS scores (an algorithmic performance count for the SSCA#2 benchmark) of 160 million and 90 million respectively, which corresponds to more than a 2X performance improvement over the previous parallel implementations. To better characterize the performance of these multithreaded systems, we correlate the SSCA#2 performance results with data from the memory-intensive STREAM and RandomAccess benchmarks. Finally, we demonstrate the applicability of our implementation to analyze massive real-world datasets by computing approximate betweenness centrality for a large-scale IMDb movie-actor network.« less

A Faster Parallel Algorithm and Efficient Multithreaded Implementations for Evaluating Betweenness Centrality on Massive Datasets

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

Madduri, Kamesh; Ediger, David; Jiang, Karl

2009-05-29

We present a new lock-free parallel algorithm for computing betweenness centrality of massive small-world networks. With minor changes to the data structures, our algorithm also achieves better spatial cache locality compared to previous approaches. Betweenness centrality is a key algorithm kernel in the HPCS SSCA#2 Graph Analysis benchmark, which has been extensively used to evaluate the performance of emerging high-performance computing architectures for graph-theoretic computations. We design optimized implementations of betweenness centrality and the SSCA#2 benchmark for two hardware multithreaded systems: a Cray XMT system with the ThreadStorm processor, and a single-socket Sun multicore server with the UltraSparc T2 processor.more » For a small-world network of 134 million vertices and 1.073 billion edges, the 16-processor XMT system and the 8-core Sun Fire T5120 server achieve TEPS scores (an algorithmic performance count for the SSCA#2 benchmark) of 160 million and 90 million respectively, which corresponds to more than a 2X performance improvement over the previous parallel implementations. To better characterize the performance of these multithreaded systems, we correlate the SSCA#2 performance results with data from the memory-intensive STREAM and RandomAccess benchmarks. Finally, we demonstrate the applicability of our implementation to analyze massive real-world datasets by computing approximate betweenness centrality for a large-scale IMDb movie-actor network.« less

TinyOS-based quality of service management in wireless sensor networks

USGS Publications Warehouse

Peterson, N.; Anusuya-Rangappa, L.; Shirazi, B.A.; Huang, R.; Song, W.-Z.; Miceli, M.; McBride, D.; Hurson, A.; LaHusen, R.

2009-01-01

Previously the cost and extremely limited capabilities of sensors prohibited Quality of Service (QoS) implementations in wireless sensor networks. With advances in technology, sensors are becoming significantly less expensive and the increases in computational and storage capabilities are opening the door for new, sophisticated algorithms to be implemented. Newer sensor network applications require higher data rates with more stringent priority requirements. We introduce a dynamic scheduling algorithm to improve bandwidth for high priority data in sensor networks, called Tiny-DWFQ. Our Tiny-Dynamic Weighted Fair Queuing scheduling algorithm allows for dynamic QoS for prioritized communications by continually adjusting the treatment of communication packages according to their priorities and the current level of network congestion. For performance evaluation, we tested Tiny-DWFQ, Tiny-WFQ (traditional WFQ algorithm implemented in TinyOS), and FIFO queues on an Imote2-based wireless sensor network and report their throughput and packet loss. Our results show that Tiny-DWFQ performs better in all test cases. ?? 2009 IEEE.

An Embedded Multi-Agent Systems Based Industrial Wireless Sensor Network

PubMed Central

Brennan, Robert W.

2017-01-01

With the emergence of cyber-physical systems, there has been a growing interest in network-connected devices. One of the key requirements of a cyber-physical device is the ability to sense its environment. Wireless sensor networks are a widely-accepted solution for this requirement. In this study, an embedded multi-agent systems-managed wireless sensor network is presented. A novel architecture is proposed, along with a novel wireless sensor network architecture. Active and passive wireless sensor node types are defined, along with their communication protocols, and two application-specific examples are presented. A series of three experiments is conducted to evaluate the performance of the agent-embedded wireless sensor network. PMID:28906452

Estimating standard errors in feature network models.

PubMed

Frank, Laurence E; Heiser, Willem J

2007-05-01

Feature network models are graphical structures that represent proximity data in a discrete space while using the same formalism that is the basis of least squares methods employed in multidimensional scaling. Existing methods to derive a network model from empirical data only give the best-fitting network and yield no standard errors for the parameter estimates. The additivity properties of networks make it possible to consider the model as a univariate (multiple) linear regression problem with positivity restrictions on the parameters. In the present study, both theoretical and empirical standard errors are obtained for the constrained regression parameters of a network model with known features. The performance of both types of standard error is evaluated using Monte Carlo techniques.

An Embedded Multi-Agent Systems Based Industrial Wireless Sensor Network.

PubMed

Taboun, Mohammed S; Brennan, Robert W

2017-09-14

With the emergence of cyber-physical systems, there has been a growing interest in network-connected devices. One of the key requirements of a cyber-physical device is the ability to sense its environment. Wireless sensor networks are a widely-accepted solution for this requirement. In this study, an embedded multi-agent systems-managed wireless sensor network is presented. A novel architecture is proposed, along with a novel wireless sensor network architecture. Active and passive wireless sensor node types are defined, along with their communication protocols, and two application-specific examples are presented. A series of three experiments is conducted to evaluate the performance of the agent-embedded wireless sensor network.

Research in computer science

NASA Technical Reports Server (NTRS)

Ortega, J. M.

1984-01-01

The research efforts of University of Virginia students under a NASA sponsored program are summarized and the status of the program is reported. The research includes: testing method evaluations for N version programming; a representation scheme for modeling three dimensional objects; fault tolerant protocols for real time local area networks; performance investigation of Cyber network; XFEM implementation; and vectorizing incomplete Cholesky conjugate gradients.

Networks and landscapes: a framework for setting goals and evaluating performance at the large landscape scale

Treesearch

R Patrick Bixler; Shawn Johnson; Kirk Emerson; Tina Nabatchi; Melly Reuling; Charles Curtin; Michele Romolini; Morgan Grove

2016-01-01

The objective of large landscape conser vation is to mitigate complex ecological problems through interventions at multiple and overlapping scales. Implementation requires coordination among a diverse network of individuals and organizations to integrate local-scale conservation activities with broad-scale goals. This requires an understanding of the governance options...

Data Transfer Advisor with Transport Profiling Optimization

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

Rao, Nageswara S.; Liu, Qiang; Yun, Daqing

The network infrastructures have been rapidly upgraded in many high-performance networks (HPNs). However, such infrastructure investment has not led to corresponding performance improvement in big data transfer, especially at the application layer, largely due to the complexity of optimizing transport control on end hosts. We design and implement ProbData, a PRofiling Optimization Based DAta Transfer Advisor, to help users determine the most effective data transfer method with the most appropriate control parameter values to achieve the best data transfer performance. ProbData employs a profiling optimization based approach to exploit the optimal operational zone of various data transfer methods in supportmore » of big data transfer in extreme scale scientific applications. We present a theoretical framework of the optimized profiling approach employed in ProbData as wellas its detailed design and implementation. The advising procedure and performance benefits of ProbData are illustrated and evaluated by proof-of-concept experiments in real-life networks.« less

Validating the TeleStroke Mimic Score: A Prediction Rule for Identifying Stroke Mimics Evaluated Over Telestroke Networks.

PubMed

Ali, Syed F; Hubert, Gordian J; Switzer, Jeffrey A; Majersik, Jennifer J; Backhaus, Roland; Shepard, L Wylie; Vedala, Kishore; Schwamm, Lee H

2018-03-01

Up to 30% of acute stroke evaluations are deemed stroke mimics, and these are common in telestroke as well. We recently published a risk prediction score for use during telestroke encounters to differentiate stroke mimics from ischemic cerebrovascular disease derived and validated in the Partners TeleStroke Network. Using data from 3 distinct US and European telestroke networks, we sought to externally validate the TeleStroke Mimic (TM) score in a broader population. We evaluated the TM score in 1930 telestroke consults from the University of Utah, Georgia Regents University, and the German TeleMedical Project for Integrative Stroke Care Network. We report the area under the curve in receiver-operating characteristic curve analysis with 95% confidence interval for our previously derived TM score in which lower TM scores correspond with a higher likelihood of being a stroke mimic. Based on final diagnosis at the end of the telestroke consultation, there were 630 of 1930 (32.6%) stroke mimics in the external validation cohort. All 6 variables included in the score were significantly different between patients with ischemic cerebrovascular disease versus stroke mimics. The TM score performed well (area under curve, 0.72; 95% confidence interval, 0.70-0.73; P <0.001), similar to our prior external validation in the Partners National Telestroke Network. The TM score's ability to predict the presence of a stroke mimic during telestroke consultation in these diverse cohorts was similar to its performance in our original cohort. Predictive decision-support tools like the TM score may help highlight key clinical differences between mimics and patients with stroke during complex, time-critical telestroke evaluations. © 2018 American Heart Association, Inc.

Telecommunication Platforms for Transmitting Sensor Data over Communication Networks-State of the Art and Challenges.

PubMed

Staniec, Kamil; Habrych, Marcin

2016-07-19

The importance of constructing wide-area sensor networks for holistic environmental state evaluation has been demonstrated. A general structure of such a network has been presented with distinction of three segments: local (based on ZigBee, Ethernet and ModBus techniques), core (base on cellular technologies) and the storage/application. The implementation of these techniques requires knowledge of their technical limitations and electromagnetic compatibility issues. The former refer to ZigBee performance degradation in multi-hop transmission, whereas the latter are associated with the common electromagnetic spectrum sharing with other existing technologies or with undesired radiated emissions generated by the radio modules of the sensor network. In many cases, it is also necessary to provide a measurement station with autonomous energy source, such as solar. As stems from measurements of the energetic efficiency of these sources, one should apply them with care and perform detailed power budget since their real performance may turn out to be far from expected. This, in turn, may negatively affect-in particular-the operation of chemical sensors implemented in the network as they often require additional heating.

Comparison of RF spectrum prediction methods for dynamic spectrum access

NASA Astrophysics Data System (ADS)

Kovarskiy, Jacob A.; Martone, Anthony F.; Gallagher, Kyle A.; Sherbondy, Kelly D.; Narayanan, Ram M.

2017-05-01

Dynamic spectrum access (DSA) refers to the adaptive utilization of today's busy electromagnetic spectrum. Cognitive radio/radar technologies require DSA to intelligently transmit and receive information in changing environments. Predicting radio frequency (RF) activity reduces sensing time and energy consumption for identifying usable spectrum. Typical spectrum prediction methods involve modeling spectral statistics with Hidden Markov Models (HMM) or various neural network structures. HMMs describe the time-varying state probabilities of Markov processes as a dynamic Bayesian network. Neural Networks model biological brain neuron connections to perform a wide range of complex and often non-linear computations. This work compares HMM, Multilayer Perceptron (MLP), and Recurrent Neural Network (RNN) algorithms and their ability to perform RF channel state prediction. Monte Carlo simulations on both measured and simulated spectrum data evaluate the performance of these algorithms. Generalizing spectrum occupancy as an alternating renewal process allows Poisson random variables to generate simulated data while energy detection determines the occupancy state of measured RF spectrum data for testing. The results suggest that neural networks achieve better prediction accuracy and prove more adaptable to changing spectral statistics than HMMs given sufficient training data.

Resilient Monitoring Systems: Architecture, Design, and Application to Boiler/Turbine Plant

DOE PAGES

Garcia, Humberto E.; Lin, Wen-Chiao; Meerkov, Semyon M.; ...

2014-11-01

Resilient monitoring systems, considered in this paper, are sensor networks that degrade gracefully under malicious attacks on their sensors, causing them to project misleading information. The goal of this work is to design, analyze, and evaluate the performance of a resilient monitoring system intended to monitor plant conditions (normal or anomalous). The architecture developed consists of four layers: data quality assessment, process variable assessment, plant condition assessment, and sensor network adaptation. Each of these layers is analyzed by either analytical or numerical tools. The performance of the overall system is evaluated using a simplified boiler/turbine plant. The measure of resiliencymore » is quantified using Kullback-Leibler divergence, and is shown to be sufficiently high in all scenarios considered.« less

Resilient monitoring systems: architecture, design, and application to boiler/turbine plant.

PubMed

Garcia, Humberto E; Lin, Wen-Chiao; Meerkov, Semyon M; Ravichandran, Maruthi T

2014-11-01

Resilient monitoring systems, considered in this paper, are sensor networks that degrade gracefully under malicious attacks on their sensors, causing them to project misleading information. The goal of this paper is to design, analyze, and evaluate the performance of a resilient monitoring system intended to monitor plant conditions (normal or anomalous). The architecture developed consists of four layers: data quality assessment, process variable assessment, plant condition assessment, and sensor network adaptation. Each of these layers is analyzed by either analytical or numerical tools. The performance of the overall system is evaluated using a simplified boiler/turbine plant. The measure of resiliency is quantified based on the Kullback-Leibler divergence and shown to be sufficiently high in all scenarios considered.

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.

Neural network for control of rearrangeable Clos networks.

PubMed

Park, Y K; Cherkassky, V

1994-09-01

Rapid evolution in the field of communication networks requires high speed switching technologies. This involves a high degree of parallelism in switching control and routing performed at the hardware level. The multistage crossbar networks have always been attractive to switch designers. In this paper a neural network approach to controlling a three-stage Clos network in real time is proposed. This controller provides optimal routing of communication traffic requests on a call-by-call basis by rearranging existing connections, with a minimum length of rearrangement sequence so that a new blocked call request can be accommodated. The proposed neural network controller uses Paull's rearrangement algorithm, along with the special (least used) switch selection rule in order to minimize the length of rearrangement sequences. The functional behavior of our model is verified by simulations and it is shown that the convergence time required for finding an optimal solution is constant, regardless of the switching network size. The performance is evaluated for random traffic with various traffic loads. Simulation results show that applying the least used switch selection rule increases the efficiency in switch rearrangements, reducing the network convergence time. The implementation aspects are also discussed to show the feasibility of the proposed approach.

Design of Neural Networks for Fast Convergence and Accuracy: Dynamics and Control

NASA Technical Reports Server (NTRS)

Maghami, Peiman G.; Sparks, Dean W., Jr.

1997-01-01

A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once properly trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component/spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

Design of neural networks for fast convergence and accuracy: dynamics and control.

PubMed

Maghami, P G; Sparks, D R

2000-01-01

A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once properly trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component/spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

A link-adding strategy for transport efficiency of complex networks

NASA Astrophysics Data System (ADS)

Ma, Jinlong; Han, Weizhan; Guo, Qing; Wang, Zhenyong; Zhang, Shuai

2016-12-01

The transport efficiency is one of the critical parameters to evaluate the performance of a network. In this paper, we propose an improved efficient (IE) strategy to enhance the network transport efficiency of complex networks by adding a fraction of links to an existing network based on the node’s local degree centrality and the shortest path length. Simulation results show that the proposed strategy can bring better traffic capacity and shorter average shortest path length than the low-degree-first (LDF) strategy under the shortest path routing protocol. It is found that the proposed strategy is beneficial to the improvement of overall traffic handling and delivering ability of the network. This study can alleviate the congestion in networks, and is helpful to design and optimize realistic networks.

Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance

PubMed Central

Ley-Bosch, Carlos; Alonso-González, Itziar; Sánchez-Rodríguez, David; Ramírez-Casañas, Carlos

2016-01-01

In the last few years, the increasing use of LEDs in illumination systems has been conducted due to the emergence of Visible Light Communication (VLC) technologies, in which data communication is performed by transmitting through the visible band of the electromagnetic spectrum. In 2011, the Institute of Electrical and Electronics Engineers (IEEE) published the IEEE 802.15.7 standard for Wireless Personal Area Networks based on VLC. Due to limitations in the coverage of the transmitted signal, wireless networks can suffer from the hidden node problems, when there are nodes in the network whose transmissions are not detected by other nodes. This problem can cause an important degradation in communications when they are made by means of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) access control method, which is used in IEEE 802.15.7 This research work evaluates the effects of the hidden node problem in the performance of the IEEE 802.15.7 standard We implement a simulator and analyze VLC performance in terms of parameters like end-to-end goodput and message loss rate. As part of this research work, a solution to the hidden node problem is proposed, based on the use of idle patterns defined in the standard. Idle patterns are sent by the network coordinator node to communicate to the other nodes that there is an ongoing transmission. The validity of the proposed solution is demonstrated with simulation results. PMID:26861352

Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance.

PubMed

Ley-Bosch, Carlos; Alonso-González, Itziar; Sánchez-Rodríguez, David; Ramírez-Casañas, Carlos

2016-02-06

In the last few years, the increasing use of LEDs in illumination systems has been conducted due to the emergence of Visible Light Communication (VLC) technologies, in which data communication is performed by transmitting through the visible band of the electromagnetic spectrum. In 2011, the Institute of Electrical and Electronics Engineers (IEEE) published the IEEE 802.15.7 standard for Wireless Personal Area Networks based on VLC. Due to limitations in the coverage of the transmitted signal, wireless networks can suffer from the hidden node problems, when there are nodes in the network whose transmissions are not detected by other nodes. This problem can cause an important degradation in communications when they are made by means of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) access control method, which is used in IEEE 802.15.7 This research work evaluates the effects of the hidden node problem in the performance of the IEEE 802.15.7 standard We implement a simulator and analyze VLC performance in terms of parameters like end-to-end goodput and message loss rate. As part of this research work, a solution to the hidden node problem is proposed, based on the use of idle patterns defined in the standard. Idle patterns are sent by the network coordinator node to communicate to the other nodes that there is an ongoing transmission. The validity of the proposed solution is demonstrated with simulation results.

Mathematical Modeling and Evaluation of Human Motions in Physical Therapy Using Mixture Density Neural Networks

PubMed Central

Vakanski, A; Ferguson, JM; Lee, S

2016-01-01

Objective The objective of the proposed research is to develop a methodology for modeling and evaluation of human motions, which will potentially benefit patients undertaking a physical rehabilitation therapy (e.g., following a stroke or due to other medical conditions). The ultimate aim is to allow patients to perform home-based rehabilitation exercises using a sensory system for capturing the motions, where an algorithm will retrieve the trajectories of a patient’s exercises, will perform data analysis by comparing the performed motions to a reference model of prescribed motions, and will send the analysis results to the patient’s physician with recommendations for improvement. Methods The modeling approach employs an artificial neural network, consisting of layers of recurrent neuron units and layers of neuron units for estimating a mixture density function over the spatio-temporal dependencies within the human motion sequences. Input data are sequences of motions related to a prescribed exercise by a physiotherapist to a patient, and recorded with a motion capture system. An autoencoder subnet is employed for reducing the dimensionality of captured sequences of human motions, complemented with a mixture density subnet for probabilistic modeling of the motion data using a mixture of Gaussian distributions. Results The proposed neural network architecture produced a model for sets of human motions represented with a mixture of Gaussian density functions. The mean log-likelihood of observed sequences was employed as a performance metric in evaluating the consistency of a subject’s performance relative to the reference dataset of motions. A publically available dataset of human motions captured with Microsoft Kinect was used for validation of the proposed method. Conclusion The article presents a novel approach for modeling and evaluation of human motions with a potential application in home-based physical therapy and rehabilitation. The described approach employs the recent progress in the field of machine learning and neural networks in developing a parametric model of human motions, by exploiting the representational power of these algorithms to encode nonlinear input-output dependencies over long temporal horizons. PMID:28111643

Mathematical Modeling and Evaluation of Human Motions in Physical Therapy Using Mixture Density Neural Networks.

PubMed

Vakanski, A; Ferguson, J M; Lee, S

2016-12-01

The objective of the proposed research is to develop a methodology for modeling and evaluation of human motions, which will potentially benefit patients undertaking a physical rehabilitation therapy (e.g., following a stroke or due to other medical conditions). The ultimate aim is to allow patients to perform home-based rehabilitation exercises using a sensory system for capturing the motions, where an algorithm will retrieve the trajectories of a patient's exercises, will perform data analysis by comparing the performed motions to a reference model of prescribed motions, and will send the analysis results to the patient's physician with recommendations for improvement. The modeling approach employs an artificial neural network, consisting of layers of recurrent neuron units and layers of neuron units for estimating a mixture density function over the spatio-temporal dependencies within the human motion sequences. Input data are sequences of motions related to a prescribed exercise by a physiotherapist to a patient, and recorded with a motion capture system. An autoencoder subnet is employed for reducing the dimensionality of captured sequences of human motions, complemented with a mixture density subnet for probabilistic modeling of the motion data using a mixture of Gaussian distributions. The proposed neural network architecture produced a model for sets of human motions represented with a mixture of Gaussian density functions. The mean log-likelihood of observed sequences was employed as a performance metric in evaluating the consistency of a subject's performance relative to the reference dataset of motions. A publically available dataset of human motions captured with Microsoft Kinect was used for validation of the proposed method. The article presents a novel approach for modeling and evaluation of human motions with a potential application in home-based physical therapy and rehabilitation. The described approach employs the recent progress in the field of machine learning and neural networks in developing a parametric model of human motions, by exploiting the representational power of these algorithms to encode nonlinear input-output dependencies over long temporal horizons.

Assessing Routing Strategies for Cognitive Radio Sensor Networks

PubMed Central

Zubair, Suleiman; Fisal, Norsheila; Baguda, Yakubu S.; Saleem, Kashif

2013-01-01

Interest in the cognitive radio sensor network (CRSN) paradigm has gradually grown among researchers. This concept seeks to fuse the benefits of dynamic spectrum access into the sensor network, making it a potential player in the next generation (NextGen) network, which is characterized by ubiquity. Notwithstanding its massive potential, little research activity has been dedicated to the network layer. By contrast, we find recent research trends focusing on the physical layer, the link layer and the transport layers. The fact that the cross-layer approach is imperative, due to the resource-constrained nature of CRSNs, can make the design of unique solutions non-trivial in this respect. This paper seeks to explore possible design opportunities with wireless sensor networks (WSNs), cognitive radio ad-hoc networks (CRAHNs) and cross-layer considerations for implementing viable CRSN routing solutions. Additionally, a detailed performance evaluation of WSN routing strategies in a cognitive radio environment is performed to expose research gaps. With this work, we intend to lay a foundation for developing CRSN routing solutions and to establish a basis for future work in this area. PMID:24077319

High-accuracy local positioning network for the alignment of the Mu2e experiment.

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

Hejdukova, Jana B.

This Diploma thesis describes the establishment of a high-precision local positioning network and accelerator alignment for the Mu2e physics experiment. The process of establishing new network consists of few steps: design of the network, pre-analysis, installation works, measurements of the network and making adjustments. Adjustments were performed using two approaches. First is a geodetic approach of taking into account the Earth’s curvature and the metrological approach of a pure 3D Cartesian system on the other side. The comparison of those two approaches is performed and evaluated in the results and compared with expected differences. The effect of the Earth’s curvaturemore » was found to be significant for this kind of network and should not be neglected. The measurements were obtained with Absolute Tracker AT401, leveling instrument Leica DNA03 and gyrotheodolite DMT Gyromat 2000. The coordinates of the points of the reference network were determined by the Least Square Meth od and the overall view is attached as Annexes.« less

Filtering Gene Ontology semantic similarity for identifying protein complexes in large protein interaction networks.

PubMed

Wang, Jian; Xie, Dong; Lin, Hongfei; Yang, Zhihao; Zhang, Yijia

2012-06-21

Many biological processes recognize in particular the importance of protein complexes, and various computational approaches have been developed to identify complexes from protein-protein interaction (PPI) networks. However, high false-positive rate of PPIs leads to challenging identification. A protein semantic similarity measure is proposed in this study, based on the ontology structure of Gene Ontology (GO) terms and GO annotations to estimate the reliability of interactions in PPI networks. Interaction pairs with low GO semantic similarity are removed from the network as unreliable interactions. Then, a cluster-expanding algorithm is used to detect complexes with core-attachment structure on filtered network. Our method is applied to three different yeast PPI networks. The effectiveness of our method is examined on two benchmark complex datasets. Experimental results show that our method performed better than other state-of-the-art approaches in most evaluation metrics. The method detects protein complexes from large scale PPI networks by filtering GO semantic similarity. Removing interactions with low GO similarity significantly improves the performance of complex identification. The expanding strategy is also effective to identify attachment proteins of complexes.

The application of artificial neural networks and support vector regression for simultaneous spectrophotometric determination of commercial eye drop contents

NASA Astrophysics Data System (ADS)

Valizadeh, Maryam; Sohrabi, Mahmoud Reza

2018-03-01

In the present study, artificial neural networks (ANNs) and support vector regression (SVR) as intelligent methods coupled with UV spectroscopy for simultaneous quantitative determination of Dorzolamide (DOR) and Timolol (TIM) in eye drop. Several synthetic mixtures were analyzed for validating the proposed methods. At first, neural network time series, which one type of network from the artificial neural network was employed and its efficiency was evaluated. Afterwards, the radial basis network was applied as another neural network. Results showed that the performance of this method is suitable for predicting. Finally, support vector regression was proposed to construct the Zilomole prediction model. Also, root mean square error (RMSE) and mean recovery (%) were calculated for SVR method. Moreover, the proposed methods were compared to the high-performance liquid chromatography (HPLC) as a reference method. One way analysis of variance (ANOVA) test at the 95% confidence level applied to the comparison results of suggested and reference methods that there were no significant differences between them. Also, the effect of interferences was investigated in spike solutions.

[Evaluation on running status of Chinese Polio Laboratories Network in 2008].

PubMed

Zhu, Shuang-li; Yan, Dong-mei; Zhu, Hui

2010-04-01

In order to evaluate the running status and provide the laboratory data for maintaining polio-free status in China, the virology surveillance database of Chinese Polio Laboratories Network (not include Hong Kong, Macao, and Taiwan)in 2008 were analyzed. The case investigation data of Acute Flaccid Paralysis(AFP)cases reported by 31 provinces (municipal, autonomous regions) through EPI surveillance information management system and the database of National Polio Laboratory (NPL) were analyzed, and the indicators of running status of Chinese Polio Laboratories Network were evaluated. 10,116 stool samples were collected from 5116 AFP cases by Chinese Polio Laboratories Network in 2008, and viral isolation and identification of all stool samples were done according to 4th World Health Organization (WHO) Polio Laboratory Manual. The rate of viral isolation and identification performed within 28d was 94.9%. 189 polioviruses (PV) and 597 of non-polio enteroviruses (NPEV) were isolated from AFP cases, the isolatien rates were 3.72% and 11.74% respectively. 251 polio positive isolates were sent to NPL from 31 provincial polio laboratories. There were 318 single serotype PVs were performed VPI sequencing. And no wild polioviruses and Vaccine-derived Polioviruses (VDPVs) were found in 2008. NPL passed the proficiency test and got full accreditation for on-site review by WHO experts in 2008. All 31 provincial Polio laboratories passed the proficiency test with the same panel as NPL, and 13 provincial Polio laboratories joined and passed the on-site review by WHO experts. The running status of Chinese Polio Laboratories Network was good, polio-free status was maintained in China in 2008. The Chinese polio laboratories network running is normaly, the laboratory surveillance system was sensitive and laboratory data were provided for maintaining the polio-free status in China.

Facilities | Transportation Research | NREL

Science.gov Websites

detailed chemical characterization, performance property measurements, and stability research. Photo of Technology Evaluation Center This off-network data center provides secure management, storage, and processing

Heterogeneous Wireless Mesh Network Technology Evaluation for Space Proximity and Surface Applications

NASA Technical Reports Server (NTRS)

DeCristofaro, Michael A.; Lansdowne, Chatwin A.; Schlesinger, Adam M.

2014-01-01

NASA has identified standardized wireless mesh networking as a key technology for future human and robotic space exploration. Wireless mesh networks enable rapid deployment, provide coverage in undeveloped regions. Mesh networks are also self-healing, resilient, and extensible, qualities not found in traditional infrastructure-based networks. Mesh networks can offer lower size, weight, and power (SWaP) than overlapped infrastructure-perapplication. To better understand the maturity, characteristics and capability of the technology, we developed an 802.11 mesh network consisting of a combination of heterogeneous commercial off-the-shelf devices and opensource firmware and software packages. Various streaming applications were operated over the mesh network, including voice and video, and performance measurements were made under different operating scenarios. During the testing several issues with the currently implemented mesh network technology were identified and outlined for future work.

Prediction of moving bed biofilm reactor (MBBR) performance for the treatment of aniline using artificial neural networks (ANN).

PubMed

Delnavaz, M; Ayati, B; Ganjidoust, H

2010-07-15

In this study, the results of 1-year efficiency forecasting using artificial neural networks (ANN) models of a moving bed biofilm reactor (MBBR) for a toxic and hard biodegradable aniline removal were investigated. The reactor was operated in an aerobic batch and continuous condition with 50% by volume which was filled with light expanded clay aggregate (LECA) as carrier. Efficiency evaluation of the reactors was obtained at different retention time (RT) of 8, 24, 48 and 72 h with an influent COD from 100 to 4000 mg/L. Exploratory data analysis was used to detect relationships between the data and dependent evaluated one. The appropriate architecture of the neural network models was determined using several steps of training and testing of the models. The ANN-based models were found to provide an efficient and a robust tool in predicting MBBR performance for treating aromatic amine compounds. 2010 Elsevier B.V. All rights reserved.

Enhanced Precision Time Synchronization for Wireless Sensor Networks

PubMed Central

Cho, Hyuntae; Kim, Jongdeok; Baek, Yunju

2011-01-01

Time synchronization in wireless sensor networks (WSNs) is a fundamental issue for the coordination of distributed entities and events. Nondeterministic latency, which may decrease the accuracy and precision of time synchronization can occur at any point in the network layers. Specially, random back-off by channel contention leads to a large uncertainty. In order to reduce the large nondeterministic uncertainty from channel contention, we propose an enhanced precision time synchronization protocol in this paper. The proposed method reduces the traffic needed for the synchronization procedure by selectively forwarding the packet. Furthermore, the time difference between sensor nodes increases as time advances because of the use of a clock source with a cheap crystal oscillator. In addition, we provide a means to maintain accurate time by adopting hardware-assisted time stamp and drift correction. Experiments are conducted to evaluate the performance of the proposed method, for which sensor nodes are designed and implemented. According to the evaluation results, the performance of the proposed method is better than that of a traditional time synchronization protocol. PMID:22164035

Enhanced precision time synchronization for wireless sensor networks.

PubMed

Cho, Hyuntae; Kim, Jongdeok; Baek, Yunju

2011-01-01

Time synchronization in wireless sensor networks (WSNs) is a fundamental issue for the coordination of distributed entities and events. Nondeterministic latency, which may decrease the accuracy and precision of time synchronization can occur at any point in the network layers. Specially, random back-off by channel contention leads to a large uncertainty. In order to reduce the large nondeterministic uncertainty from channel contention, we propose an enhanced precision time synchronization protocol in this paper. The proposed method reduces the traffic needed for the synchronization procedure by selectively forwarding the packet. Furthermore, the time difference between sensor nodes increases as time advances because of the use of a clock source with a cheap crystal oscillator. In addition, we provide a means to maintain accurate time by adopting hardware-assisted time stamp and drift correction. Experiments are conducted to evaluate the performance of the proposed method, for which sensor nodes are designed and implemented. According to the evaluation results, the performance of the proposed method is better than that of a traditional time synchronization protocol.

An Efficient Framework Model for Optimizing Routing Performance in VANETs

PubMed Central

Zulkarnain, Zuriati Ahmad; Subramaniam, Shamala

2018-01-01

Routing in Vehicular Ad hoc Networks (VANET) is a bit complicated because of the nature of the high dynamic mobility. The efficiency of routing protocol is influenced by a number of factors such as network density, bandwidth constraints, traffic load, and mobility patterns resulting in frequency changes in network topology. Therefore, Quality of Service (QoS) is strongly needed to enhance the capability of the routing protocol and improve the overall network performance. In this paper, we introduce a statistical framework model to address the problem of optimizing routing configuration parameters in Vehicle-to-Vehicle (V2V) communication. Our framework solution is based on the utilization of the network resources to further reflect the current state of the network and to balance the trade-off between frequent changes in network topology and the QoS requirements. It consists of three stages: simulation network stage used to execute different urban scenarios, the function stage used as a competitive approach to aggregate the weighted cost of the factors in a single value, and optimization stage used to evaluate the communication cost and to obtain the optimal configuration based on the competitive cost. The simulation results show significant performance improvement in terms of the Packet Delivery Ratio (PDR), Normalized Routing Load (NRL), Packet loss (PL), and End-to-End Delay (E2ED). PMID:29462884

Social-aware data dissemination in opportunistic mobile social networks

NASA Astrophysics Data System (ADS)

Yang, Yibo; Zhao, Honglin; Ma, Jinlong; Han, Xiaowei

Opportunistic Mobile Social Networks (OMSNs), formed by mobile users with social relationships and characteristics, enhance spontaneous communication among users that opportunistically encounter each other. Such networks can be exploited to improve the performance of data forwarding. Discovering optimal relay nodes is one of the important issues for efficient data propagation in OMSNs. Although traditional centrality definitions to identify the nodes features in network, they cannot identify effectively the influential nodes for data dissemination in OMSNs. Existing protocols take advantage of spatial contact frequency and social characteristics to enhance transmission performance. However, existing protocols have not fully exploited the benefits of the relations and the effects between geographical information, social features and user interests. In this paper, we first evaluate these three characteristics of users and design a routing protocol called Geo-Social-Interest (GSI) protocol to select optimal relay nodes. We compare the performance of GSI using real INFOCOM06 data sets. The experiment results demonstrate that GSI overperforms the other protocols with highest data delivery ratio and low communication overhead.

Investigating the quality of video consultations performed using fourth generation (4G) mobile telecommunications.

PubMed

Caffery, Liam J; Smith, Anthony C

2015-09-01

The use of fourth-generation (4G) mobile telecommunications to provide real-time video consultations were investigated in this study with the aims of determining if 4G is a suitable telecommunications technology; and secondly, to identify if variation in perceived audio and video quality were due to underlying network performance. Three patient end-points that used 4G Internet connections were evaluated. Consulting clinicians recorded their perception of audio and video quality using the International Telecommunications Union scales during clinics with these patient end-points. These scores were used to calculate a mean opinion score (MOS). The network performance metrics were obtained for each session and the relationships between these metrics and the session's quality scores were tested. Clinicians scored the quality of 50 hours of video consultations, involving 36 clinic sessions. The MOS for audio was 4.1 ± 0.62 and the MOS for video was 4.4 ± 0.22. Image impairment and effort to listen were also rated favourably. There was no correlation between audio or video quality and the network metrics of packet loss or jitter. These findings suggest that 4G networks are an appropriate telecommunication technology to deliver real-time video consultations. Variations in quality scores observed during this study were not explained by the packet loss and jitter in the underlying network. Before establishing a telemedicine service, the performance of the 4G network should be assessed at the location of the proposed service. This is due to known variability in performance of 4G networks. © The Author(s) 2015.

IBE-Lite: a lightweight identity-based cryptography for body sensor networks.

PubMed

Tan, Chiu C; Wang, Haodong; Zhong, Sheng; Li, Qun

2009-11-01

A body sensor network (BSN) is a network of sensors deployed on a person's body for health care monitoring. Since the sensors collect personal medical data, security and privacy are important components in a BSN. In this paper, we developed IBE-Lite, a lightweight identity-based encryption suitable for sensors in a BSN. We present protocols based on IBE-Lite that balance security and privacy with accessibility and perform evaluation using experiments conducted on commercially available sensors.

An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images.

PubMed

Nogueira, Mariana A; Abreu, Pedro H; Martins, Pedro; Machado, Penousal; Duarte, Hugo; Santos, João

2017-02-13

Positron Emission Tomography - Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response-to-treatment classes.

Simulation Modeling and Performance Evaluation of Space Networks

NASA Technical Reports Server (NTRS)

Jennings, Esther H.; Segui, John

2006-01-01

In space exploration missions, the coordinated use of spacecraft as communication relays increases the efficiency of the endeavors. To conduct trade-off studies of the performance and resource usage of different communication protocols and network designs, JPL designed a comprehensive extendable tool, the Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE). The design and development of MACHETE began in 2000 and is constantly evolving. Currently, MACHETE contains Consultative Committee for Space Data Systems (CCSDS) protocol standards such as Proximity-1, Advanced Orbiting Systems (AOS), Packet Telemetry/Telecommand, Space Communications Protocol Specification (SCPS), and the CCSDS File Delivery Protocol (CFDP). MACHETE uses the Aerospace Corporation s Satellite Orbital Analysis Program (SOAP) to generate the orbital geometry information and contact opportunities. Matlab scripts provide the link characteristics. At the core of MACHETE is a discrete event simulator, QualNet. Delay Tolerant Networking (DTN) is an end-to-end architecture providing communication in and/or through highly stressed networking environments. Stressed networking environments include those with intermittent connectivity, large and/or variable delays, and high bit error rates. To provide its services, the DTN protocols reside at the application layer of the constituent internets, forming a store-and-forward overlay network. The key capabilities of the bundling protocols include custody-based reliability, ability to cope with intermittent connectivity, ability to take advantage of scheduled and opportunistic connectivity, and late binding of names to addresses. In this presentation, we report on the addition of MACHETE models needed to support DTN, namely: the Bundle Protocol (BP) model. To illustrate the use of MACHETE with the additional DTN model, we provide an example simulation to benchmark its performance. We demonstrate the use of the DTN protocol and discuss statistics gathered concerning the total time needed to simulate numerous bundle transmissions

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.

Performance Evaluation of an Enhanced Uplink 3.5G System for Mobile Healthcare Applications.

PubMed

Komnakos, Dimitris; Vouyioukas, Demosthenes; Maglogiannis, Ilias; Constantinou, Philip

2008-01-01

The present paper studies the prospective and the performance of a forthcoming high-speed third generation (3.5G) networking technology, called enhanced uplink, for delivering mobile health (m-health) applications. The performance of 3.5G networks is a critical factor for successful development of m-health services perceived by end users. In this paper, we propose a methodology for performance assessment based on the joint uplink transmission of voice, real-time video, biological data (such as electrocardiogram, vital signals, and heart sounds), and healthcare records file transfer. Various scenarios were concerned in terms of real-time, nonreal-time, and emergency applications in random locations, where no other system but 3.5G is available. The accomplishment of quality of service (QoS) was explored through a step-by-step improvement of enhanced uplink system's parameters, attributing the network system for the best performance in the context of the desired m-health services.

Performance Evaluation of an Enhanced Uplink 3.5G System for Mobile Healthcare Applications

PubMed Central

Komnakos, Dimitris; Vouyioukas, Demosthenes; Maglogiannis, Ilias; Constantinou, Philip

2008-01-01

The present paper studies the prospective and the performance of a forthcoming high-speed third generation (3.5G) networking technology, called enhanced uplink, for delivering mobile health (m-health) applications. The performance of 3.5G networks is a critical factor for successful development of m-health services perceived by end users. In this paper, we propose a methodology for performance assessment based on the joint uplink transmission of voice, real-time video, biological data (such as electrocardiogram, vital signals, and heart sounds), and healthcare records file transfer. Various scenarios were concerned in terms of real-time, nonreal-time, and emergency applications in random locations, where no other system but 3.5G is available. The accomplishment of quality of service (QoS) was explored through a step-by-step improvement of enhanced uplink system's parameters, attributing the network system for the best performance in the context of the desired m-health services. PMID:19132096

Health Insurance Portability and Accountability Act-Compliant Ocular Telehealth Network for the Remote Diagnosis and Management of Diabetic Retinopathy

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

Li, Yaquin; Karnowski, Thomas Paul; Tobin Jr, Kenneth William

2011-01-01

In this article, we present the design and implementation of a regional ocular telehealth network for remote assessment and management of diabetic retinopathy (DR), including the design requirements, network topology, protocol design, system work flow, graphics user interfaces, and performance evaluation. The Telemedical Retinal Image Analysis and Diagnosis Network is a computer-aided, image analysis telehealth paradigm for the diagnosis of DR and other retinal diseases using fundus images acquired from primary care end users delivering care to underserved patient populations in the mid-South and southeastern United States.

A health insurance portability and accountability act-compliant ocular telehealth network for the remote diagnosis and management of diabetic retinopathy.

PubMed

Li, Yaqin; Karnowski, Thomas P; Tobin, Kenneth W; Giancardo, Luca; Morris, Scott; Sparrow, Sylvia E; Garg, Seema; Fox, Karen; Chaum, Edward

2011-10-01

In this article, we present the design and implementation of a regional ocular telehealth network for remote assessment and management of diabetic retinopathy (DR), including the design requirements, network topology, protocol design, system work flow, graphics user interfaces, and performance evaluation. The Telemedical Retinal Image Analysis and Diagnosis Network is a computer-aided, image analysis telehealth paradigm for the diagnosis of DR and other retinal diseases using fundus images acquired from primary care end users delivering care to underserved patient populations in the mid-South and southeastern United States.

Tool wear modeling using abductive networks

NASA Astrophysics Data System (ADS)

Masory, Oren

1992-09-01

A tool wear model based on Abductive Networks, which consists of a network of `polynomial' nodes, is described. The model relates the cutting parameters, components of the cutting force, and machining time to flank wear. Thus real time measurements of the cutting force can be used to monitor the machining process. The model is obtained by a training process in which the connectivity between the network's nodes and the polynomial coefficients of each node are determined by optimizing a performance criteria. Actual wear measurements of coated and uncoated carbide inserts were used for training and evaluating the established model.

Sign Language Recognition System using Neural Network for Digital Hardware Implementation

NASA Astrophysics Data System (ADS)

Vargas, Lorena P.; Barba, Leiner; Torres, C. O.; Mattos, L.

2011-01-01

This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

Design and evaluation of a DAMQ multiprocessor network with self-compacting buffers

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

Park, J.; O`Krafka, B.W.O.; Vassiliadis, S.

1994-12-31

This paper describes a new approach to implement Dynamically Allocated Multi-Queue (DAMQ) switching elements using a technique called ``self-compacting buffers``. This technique is efficient in that the amount of hardware required to manage the buffers is relatively small; it offers high performance since it is an implementation of a DAMQ. The first part of this paper describes the self-compacting buffer architecture in detail, and compares it against a competing DAMQ switch design. The second part presents extensive simulation results comparing the performance of a self compacting buffer switch against an ideal switch including several examples of k-ary n-cubes and deltamore » networks. In addition, simulation results show how the performance of an entire network can be quickly and accurately approximated by simulating just a single switching element.« less

A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization

PubMed Central

2012-01-01

Background Graph drawing is an integral part of many systems biology studies, enabling visual exploration and mining of large-scale biological networks. While a number of layout algorithms are available in popular network analysis platforms, such as Cytoscape, it remains poorly understood how well their solutions reflect the underlying biological processes that give rise to the network connectivity structure. Moreover, visualizations obtained using conventional layout algorithms, such as those based on the force-directed drawing approach, may become uninformative when applied to larger networks with dense or clustered connectivity structure. Methods We implemented a modified layout plug-in, named Multilevel Layout, which applies the conventional layout algorithms within a multilevel optimization framework to better capture the hierarchical modularity of many biological networks. Using a wide variety of real life biological networks, we carried out a systematic evaluation of the method in comparison with other layout algorithms in Cytoscape. Results The multilevel approach provided both biologically relevant and visually pleasant layout solutions in most network types, hence complementing the layout options available in Cytoscape. In particular, it could improve drawing of large-scale networks of yeast genetic interactions and human physical interactions. In more general terms, the biological evaluation framework developed here enables one to assess the layout solutions from any existing or future graph drawing algorithm as well as to optimize their performance for a given network type or structure. Conclusions By making use of the multilevel modular organization when visualizing biological networks, together with the biological evaluation of the layout solutions, one can generate convenient visualizations for many network biology applications. PMID:22448851

Optimization of rainfall networks using information entropy and temporal variability analysis

NASA Astrophysics Data System (ADS)

Wang, Wenqi; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Liu, Jiufu; Zou, Ying; He, Ruimin

2018-04-01

Rainfall networks are the most direct sources of precipitation data and their optimization and evaluation are essential and important. Information entropy can not only represent the uncertainty of rainfall distribution but can also reflect the correlation and information transmission between rainfall stations. Using entropy this study performs optimization of rainfall networks that are of similar size located in two big cities in China, Shanghai (in Yangtze River basin) and Xi'an (in Yellow River basin), with respect to temporal variability analysis. Through an easy-to-implement greedy ranking algorithm based on the criterion called, Maximum Information Minimum Redundancy (MIMR), stations of the networks in the two areas (each area is further divided into two subareas) are ranked during sliding inter-annual series and under different meteorological conditions. It is found that observation series with different starting days affect the ranking, alluding to the temporal variability during network evaluation. We propose a dynamic network evaluation framework for considering temporal variability, which ranks stations under different starting days with a fixed time window (1-year, 2-year, and 5-year). Therefore, we can identify rainfall stations which are temporarily of importance or redundancy and provide some useful suggestions for decision makers. The proposed framework can serve as a supplement for the primary MIMR optimization approach. In addition, during different periods (wet season or dry season) the optimal network from MIMR exhibits differences in entropy values and the optimal network from wet season tended to produce higher entropy values. Differences in spatial distribution of the optimal networks suggest that optimizing the rainfall network for changing meteorological conditions may be more recommended.

Non-Orthogonal Multiple Access for Ubiquitous Wireless Sensor Networks.

PubMed

Anwar, Asim; Seet, Boon-Chong; Ding, Zhiguo

2018-02-08

Ubiquitous wireless sensor networks (UWSNs) have become a critical technology for enabling smart cities and other ubiquitous monitoring applications. Their deployment, however, can be seriously hampered by the spectrum available to the sheer number of sensors for communication. To support the communication needs of UWSNs without requiring more spectrum resources, the power-domain non-orthogonal multiple access (NOMA) technique originally proposed for 5th Generation (5G) cellular networks is investigated for UWSNs for the first time in this paper. However, unlike 5G networks that operate in the licensed spectrum, UWSNs mostly operate in unlicensed spectrum where sensors also experience cross-technology interferences from other devices sharing the same spectrum. In this paper, we model the interferences from various sources at the sensors using stochastic geometry framework. To evaluate the performance, we derive a theorem and present new closed form expression for the outage probability of the sensors in a downlink scenario under interference limited environment. In addition, diversity analysis for the ordered NOMA users is performed. Based on the derived outage probability, we evaluate the average link throughput and energy consumption efficiency of NOMA against conventional orthogonal multiple access (OMA) technique in UWSNs. Further, the required computational complexity for the NOMA users is presented.

Performance Evaluation of Relay Selection Schemes in Beacon-Assisted Dual-Hop Cognitive Radio Wireless Sensor Networks under Impact of Hardware Noises.

PubMed

Hieu, Tran Dinh; Duy, Tran Trung; Dung, Le The; Choi, Seong Gon

2018-06-05

To solve the problem of energy constraints and spectrum scarcity for cognitive radio wireless sensor networks (CR-WSNs), an underlay decode-and-forward relaying scheme is considered, where the energy constrained secondary source and relay nodes are capable of harvesting energy from a multi-antenna power beacon (PB) and using that harvested energy to forward the source information to the destination. Based on the time switching receiver architecture, three relaying protocols, namely, hybrid partial relay selection (H-PRS), conventional opportunistic relay selection (C-ORS), and best opportunistic relay selection (B-ORS) protocols are considered to enhance the end-to-end performance under the joint impact of maximal interference constraint and transceiver hardware impairments. For performance evaluation and comparison, we derive the exact and asymptotic closed-form expressions of outage probability (OP) and throughput (TP) to provide significant insights into the impact of our proposed protocols on the system performance over Rayleigh fading channel. Finally, simulation results validate the theoretical results.

LADS: Optimizing Data Transfers using Layout-Aware Data Scheduling

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

Kim, Youngjae; Atchley, Scott; Vallee, Geoffroy R

While future terabit networks hold the promise of signifi- cantly improving big-data motion among geographically distributed data centers, significant challenges must be overcome even on today s 100 gigabit networks to real- ize end-to-end performance. Multiple bottlenecks exist along the end-to-end path from source to sink. Data stor- age infrastructure at both the source and sink and its in- terplay with the wide-area network are increasingly the bottleneck to achieving high performance. In this paper, we identify the issues that lead to congestion on the path of an end-to-end data transfer in the terabit network en- vironment, and we presentmore » a new bulk data movement framework called LADS for terabit networks. LADS ex- ploits the underlying storage layout at each endpoint to maximize throughput without negatively impacting the performance of shared storage resources for other users. LADS also uses the Common Communication Interface (CCI) in lieu of the sockets interface to use zero-copy, OS-bypass hardware when available. It can further im- prove data transfer performance under congestion on the end systems using buffering at the source using flash storage. With our evaluations, we show that LADS can avoid congested storage elements within the shared stor- age resource, improving I/O bandwidth, and data transfer rates across the high speed networks.« less

Mobility based multicast routing in wireless mesh networks

NASA Astrophysics Data System (ADS)

Jain, Sanjeev; Tripathi, Vijay S.; Tiwari, Sudarshan

2013-01-01

There exist two fundamental approaches to multicast routing namely minimum cost trees and shortest path trees. The (MCT's) minimum cost tree is one which connects receiver and sources by providing a minimum number of transmissions (MNTs) the MNTs approach is generally used for energy constraint sensor and mobile ad hoc networks. In this paper we have considered node mobility and try to find out simulation based comparison of the (SPT's) shortest path tree, (MST's) minimum steiner trees and minimum number of transmission trees in wireless mesh networks by using the performance metrics like as an end to end delay, average jitter, throughput and packet delivery ratio, average unicast packet delivery ratio, etc. We have also evaluated multicast performance in the small and large wireless mesh networks. In case of multicast performance in the small networks we have found that when the traffic load is moderate or high the SPTs outperform the MSTs and MNTs in all cases. The SPTs have lowest end to end delay and average jitter in almost all cases. In case of multicast performance in the large network we have seen that the MSTs provide minimum total edge cost and minimum number of transmissions. We have also found that the one drawback of SPTs, when the group size is large and rate of multicast sending is high SPTs causes more packet losses to other flows as MCTs.

Cross-layer restoration with software defined networking based on IP over optical transport networks

NASA Astrophysics Data System (ADS)

Yang, Hui; Cheng, Lei; Deng, Junni; Zhao, Yongli; Zhang, Jie; Lee, Young

2015-10-01

The IP over optical transport network is a very promising networking architecture applied to the interconnection of geographically distributed data centers due to the performance guarantee of low delay, huge bandwidth and high reliability at a low cost. It can enable efficient resource utilization and support heterogeneous bandwidth demands in highly-available, cost-effective and energy-effective manner. In case of cross-layer link failure, to ensure a high-level quality of service (QoS) for user request after the failure becomes a research focus. In this paper, we propose a novel cross-layer restoration scheme for data center services with software defined networking based on IP over optical network. The cross-layer restoration scheme can enable joint optimization of IP network and optical network resources, and enhance the data center service restoration responsiveness to the dynamic end-to-end service demands. We quantitatively evaluate the feasibility and performances through the simulation under heavy traffic load scenario in terms of path blocking probability and path restoration latency. Numeric results show that the cross-layer restoration scheme improves the recovery success rate and minimizes the overall recovery time.

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.

Performance Evaluation of LoRa Considering Scenario Conditions.

PubMed

Sanchez-Iborra, Ramon; Sanchez-Gomez, Jesus; Ballesta-Viñas, Juan; Cano, Maria-Dolores; Skarmeta, Antonio F

2018-03-03

New verticals within the Internet of Things (IoT) paradigm such as smart cities, smart farming, or goods monitoring, among many others, are demanding strong requirements to the Radio Access Network (RAN) in terms of coverage, end-node's power consumption, and scalability. The technologies employed so far to provide IoT scenarios with connectivity, e.g., wireless sensor network and cellular technologies, are not able to simultaneously cope with these three requirements. Thus, a novel solution known as Low Power - Wide Area Network (LP-WAN) has emerged as a promising alternative to provide with low-cost and low-power-consumption connectivity to end-nodes spread in a wide area. Concretely, the Long-Range Wide Area Network (LoRaWAN) technology is one of the LP-WAN platforms that is receiving greater attention from both the industry and the academia. For that reason, in this work, a comprehensive performance evaluation of LoRaWAN under different environmental conditions is presented. The results are obtained from three real scenarios, namely, urban, suburban, and rural, considering both dynamic and static conditions, hence a discussion about the most proper LoRaWAN physical-layer configuration for each scenario is provided. Besides, a theoretical coverage study is also conducted by the use of a radio planning tool considering topographic maps and a precise propagation model. From the attained results, it can be concluded that it is necessary to evaluate the propagation conditions of the deployment scenario prior to the system implantation in order to reach a compromise between the robustness of the network and the transmission data-rate.

Performance Evaluation of LoRa Considering Scenario Conditions

PubMed Central

Sanchez-Gomez, Jesus; Ballesta-Viñas, Juan

2018-01-01

New verticals within the Internet of Things (IoT) paradigm such as smart cities, smart farming, or goods monitoring, among many others, are demanding strong requirements to the Radio Access Network (RAN) in terms of coverage, end-node’s power consumption, and scalability. The technologies employed so far to provide IoT scenarios with connectivity, e.g., wireless sensor network and cellular technologies, are not able to simultaneously cope with these three requirements. Thus, a novel solution known as Low Power - Wide Area Network (LP-WAN) has emerged as a promising alternative to provide with low-cost and low-power-consumption connectivity to end-nodes spread in a wide area. Concretely, the Long-Range Wide Area Network (LoRaWAN) technology is one of the LP-WAN platforms that is receiving greater attention from both the industry and the academia. For that reason, in this work, a comprehensive performance evaluation of LoRaWAN under different environmental conditions is presented. The results are obtained from three real scenarios, namely, urban, suburban, and rural, considering both dynamic and static conditions, hence a discussion about the most proper LoRaWAN physical-layer configuration for each scenario is provided. Besides, a theoretical coverage study is also conducted by the use of a radio planning tool considering topographic maps and a precise propagation model. From the attained results, it can be concluded that it is necessary to evaluate the propagation conditions of the deployment scenario prior to the system implantation in order to reach a compromise between the robustness of the network and the transmission data-rate. PMID:29510524

Implementation of an Antenna Array Signal Processing Breadboard for the Deep Space Network

NASA Technical Reports Server (NTRS)

Navarro, Robert

2006-01-01

The Deep Space Network Large Array will replace/augment 34 and 70 meter antenna assets. The array will mainly be used to support NASA's deep space telemetry, radio science, and navigation requirements. The array project will deploy three complexes in the western U.S., Australia, and European longitude each with 400 12m downlink antennas and a DSN central facility at JPL. THis facility will remotely conduct all real-time monitor and control for the network. Signal processing objectives include: provide a means to evaluate the performance of the Breadboard Array's antenna subsystem; design and build prototype hardware; demonstrate and evaluate proposed signal processing techniques; and gain experience with various technologies that may be used in the Large Array. Results are summarized..

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.

Evaluating a Chronic Disease Management Improvement Collaboration: Lessons in Design and Implementation Fundamentals.

PubMed

Phillips, Kaye; Amar, Claudia; Elicksen-Jensen, Keesa

2016-01-01

For the Canadian Foundation for Healthcare Improvement (CFHI), the Atlantic Healthcare Collaboration (AHC) was a pivotal opportunity to build upon its experience and expertise in delivering regional change management training and to apply and refine its evaluation and performance measurement approach. This paper reports on its evaluation principles and approach, as well as the lessons learned as CFHI diligently coordinated and worked with improvement project (IP) teams and a network of stakeholders to design and undertake a suite of evaluative activities. The evaluation generated evidence and learnings about various elements of chronic disease prevention and management (CDPM) improvement processes, individual and team capacity building and the role and value of CFHI in facilitating tailored learning activities and networking among teams, coaches and other AHC stakeholders.

Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks.

PubMed

Tran, Son N; d'Avila Garcez, Artur S

2018-02-01

Developments in deep learning have seen the use of layerwise unsupervised learning combined with supervised learning for fine-tuning. With this layerwise approach, a deep network can be seen as a more modular system that lends itself well to learning representations. In this paper, we investigate whether such modularity can be useful to the insertion of background knowledge into deep networks, whether it can improve learning performance when it is available, and to the extraction of knowledge from trained deep networks, and whether it can offer a better understanding of the representations learned by such networks. To this end, we use a simple symbolic language-a set of logical rules that we call confidence rules-and show that it is suitable for the representation of quantitative reasoning in deep networks. We show by knowledge extraction that confidence rules can offer a low-cost representation for layerwise networks (or restricted Boltzmann machines). We also show that layerwise extraction can produce an improvement in the accuracy of deep belief networks. Furthermore, the proposed symbolic characterization of deep networks provides a novel method for the insertion of prior knowledge and training of deep networks. With the use of this method, a deep neural-symbolic system is proposed and evaluated, with the experimental results indicating that modularity through the use of confidence rules and knowledge insertion can be beneficial to network performance.

Effects of amyloid and small vessel disease on white matter network disruption.

PubMed

Kim, Hee Jin; Im, Kiho; Kwon, Hunki; Lee, Jong Min; Ye, Byoung Seok; Kim, Yeo Jin; Cho, Hanna; Choe, Yearn Seong; Lee, Kyung Han; Kim, Sung Tae; Kim, Jae Seung; Lee, Jae Hong; Na, Duk L; Seo, Sang Won

2015-01-01

There is growing evidence that the human brain is a large scale complex network. The structural network is reported to be disrupted in cognitively impaired patients. However, there have been few studies evaluating the effects of amyloid and small vessel disease (SVD) markers, the common causes of cognitive impairment, on structural networks. Thus, we evaluated the association between amyloid and SVD burdens and structural networks using diffusion tensor imaging (DTI). Furthermore, we determined if network parameters predict cognitive impairments. Graph theoretical analysis was applied to DTI data from 232 cognitively impaired patients with varying degrees of amyloid and SVD burdens. All patients underwent Pittsburgh compound-B (PiB) PET to detect amyloid burden, MRI to detect markers of SVD, including the volume of white matter hyperintensities and the number of lacunes, and detailed neuropsychological testing. The whole-brain network was assessed by network parameters of integration (shortest path length, global efficiency) and segregation (clustering coefficient, transitivity, modularity). PiB retention ratio was not associated with any white matter network parameters. Greater white matter hyperintensity volumes or lacunae numbers were significantly associated with decreased network integration (increased shortest path length, decreased global efficiency) and increased network segregation (increased clustering coefficient, increased transitivity, increased modularity). Decreased network integration or increased network segregation were associated with poor performances in attention, language, visuospatial, memory, and frontal-executive functions. Our results suggest that SVD alters white matter network integration and segregation, which further predicts cognitive dysfunction.

A mixed integer bi-level DEA model for bank branch performance evaluation by Stackelberg approach

NASA Astrophysics Data System (ADS)

Shafiee, Morteza; Lotfi, Farhad Hosseinzadeh; Saleh, Hilda; Ghaderi, Mehdi

2016-03-01

One of the most complicated decision making problems for managers is the evaluation of bank performance, which involves various criteria. There are many studies about bank efficiency evaluation by network DEA in the literature review. These studies do not focus on multi-level network. Wu (Eur J Oper Res 207:856-864, 2010) proposed a bi-level structure for cost efficiency at the first time. In this model, multi-level programming and cost efficiency were used. He used a nonlinear programming to solve the model. In this paper, we have focused on multi-level structure and proposed a bi-level DEA model. We then used a liner programming to solve our model. In other hand, we significantly improved the way to achieve the optimum solution in comparison with the work by Wu (2010) by converting the NP-hard nonlinear programing into a mixed integer linear programming. This study uses a bi-level programming data envelopment analysis model that embodies internal structure with Stackelberg-game relationships to evaluate the performance of banking chain. The perspective of decentralized decisions is taken in this paper to cope with complex interactions in banking chain. The results derived from bi-level programming DEA can provide valuable insights and detailed information for managers to help them evaluate the performance of the banking chain as a whole using Stackelberg-game relationships. Finally, this model was applied in the Iranian bank to evaluate cost efficiency.

Prioritized Degree Distribution in Wireless Sensor Networks with a Network Coded Data Collection Method

PubMed Central

Wan, Jan; Xiong, Naixue; Zhang, Wei; Zhang, Qinchao; Wan, Zheng

2012-01-01

The reliability of wireless sensor networks (WSNs) can be greatly affected by failures of sensor nodes due to energy exhaustion or the influence of brutal external environment conditions. Such failures seriously affect the data persistence and collection efficiency. Strategies based on network coding technology for WSNs such as LTCDS can improve the data persistence without mass redundancy. However, due to the bad intermediate performance of LTCDS, a serious ‘cliff effect’ may appear during the decoding period, and source data are hard to recover from sink nodes before sufficient encoded packets are collected. In this paper, the influence of coding degree distribution strategy on the ‘cliff effect’ is observed and the prioritized data storage and dissemination algorithm PLTD-ALPHA is presented to achieve better data persistence and recovering performance. With PLTD-ALPHA, the data in sensor network nodes present a trend that their degree distribution increases along with the degree level predefined, and the persistent data packets can be submitted to the sink node according to its degree in order. Finally, the performance of PLTD-ALPHA is evaluated and experiment results show that PLTD-ALPHA can greatly improve the data collection performance and decoding efficiency, while data persistence is not notably affected. PMID:23235451

Network Speech Systems Technology Program

NASA Astrophysics Data System (ADS)

Weinstein, C. J.

1980-09-01

This report documents work performed during FY 1980 on the DCA-sponsored Network Speech Systems Technology Program. The areas of work reported are: (1) communication systems studies in Demand-Assignment Multiple Access (DAMA), voice/data integration, and adaptive routing, in support of the evolving Defense Communications System (DCS) and Defense Switched Network (DSN); (2) a satellite/terrestrial integration design study including the functional design of voice and data interfaces to interconnect terrestrial and satellite network subsystems; and (3) voice-conferencing efforts dealing with support of the Secure Voice and Graphics Conferencing (SVGC) Test and Evaluation Program. Progress in definition and planning of experiments for the Experimental Integrated Switched Network (EISN) is detailed separately in an FY 80 Experiment Plan Supplement.

Reduced kernel recursive least squares algorithm for aero-engine degradation prediction

NASA Astrophysics Data System (ADS)

Zhou, Haowen; Huang, Jinquan; Lu, Feng

2017-10-01

Kernel adaptive filters (KAFs) generate a linear growing radial basis function (RBF) network with the number of training samples, thereby lacking sparseness. To deal with this drawback, traditional sparsification techniques select a subset of original training data based on a certain criterion to train the network and discard the redundant data directly. Although these methods curb the growth of the network effectively, it should be noted that information conveyed by these redundant samples is omitted, which may lead to accuracy degradation. In this paper, we present a novel online sparsification method which requires much less training time without sacrificing the accuracy performance. Specifically, a reduced kernel recursive least squares (RKRLS) algorithm is developed based on the reduced technique and the linear independency. Unlike conventional methods, our novel methodology employs these redundant data to update the coefficients of the existing network. Due to the effective utilization of the redundant data, the novel algorithm achieves a better accuracy performance, although the network size is significantly reduced. Experiments on time series prediction and online regression demonstrate that RKRLS algorithm requires much less computational consumption and maintains the satisfactory accuracy performance. Finally, we propose an enhanced multi-sensor prognostic model based on RKRLS and Hidden Markov Model (HMM) for remaining useful life (RUL) estimation. A case study in a turbofan degradation dataset is performed to evaluate the performance of the novel prognostic approach.

Assuring SS7 dependability: A robustness characterization of signaling network elements

NASA Astrophysics Data System (ADS)

Karmarkar, Vikram V.

1994-04-01

Current and evolving telecommunication services will rely on signaling network performance and reliability properties to build competitive call and connection control mechanisms under increasing demands on flexibility without compromising on quality. The dimensions of signaling dependability most often evaluated are the Rate of Call Loss and End-to-End Route Unavailability. A third dimension of dependability that captures the concern about large or catastrophic failures can be termed Network Robustness. This paper is concerned with the dependability aspects of the evolving Signaling System No. 7 (SS7) networks and attempts to strike a balance between the probabilistic and deterministic measures that must be evaluated to accomplish a risk-trend assessment to drive architecture decisions. Starting with high-level network dependability objectives and field experience with SS7 in the U.S., potential areas of growing stringency in network element (NE) dependability are identified to improve against current measures of SS7 network quality, as per-call signaling interactions increase. A sensitivity analysis is presented to highlight the impact due to imperfect coverage of duplex network component or element failures (i.e., correlated failures), to assist in the setting of requirements on NE robustness. A benefit analysis, covering several dimensions of dependability, is used to generate the domain of solutions available to the network architect in terms of network and network element fault tolerance that may be specified to meet the desired signaling quality goals.

Seismic activity prediction using computational intelligence techniques in northern Pakistan

NASA Astrophysics Data System (ADS)

Asim, Khawaja M.; Awais, Muhammad; Martínez-Álvarez, F.; Iqbal, Talat

2017-10-01

Earthquake prediction study is carried out for the region of northern Pakistan. The prediction methodology includes interdisciplinary interaction of seismology and computational intelligence. Eight seismic parameters are computed based upon the past earthquakes. Predictive ability of these eight seismic parameters is evaluated in terms of information gain, which leads to the selection of six parameters to be used in prediction. Multiple computationally intelligent models have been developed for earthquake prediction using selected seismic parameters. These models include feed-forward neural network, recurrent neural network, random forest, multi layer perceptron, radial basis neural network, and support vector machine. The performance of every prediction model is evaluated and McNemar's statistical test is applied to observe the statistical significance of computational methodologies. Feed-forward neural network shows statistically significant predictions along with accuracy of 75% and positive predictive value of 78% in context of northern Pakistan.

New tools for non-invasive exploration of collagen network in cartilaginous tissue-engineered substitute.

PubMed

Henrionnet, Christel; Dumas, Dominique; Hupont, Sébastien; Stoltz, Jean François; Mainard, Didier; Gillet, Pierre; Pinzano, Astrid

2017-01-01

In tissue engineering approaches, the quality of substitutes is a key element to determine its ability to treat cartilage defects. However, in clinical practice, the evaluation of tissue-engineered cartilage substitute quality is not possible due to the invasiveness of the standard procedure, which is to date histology. The aim of this work was to validate a new innovative system performed from two-photon excitation laser adapted to an optical macroscope to evaluate at macroscopic scale the collagen network in cartilage tissue-engineered substitutes in confrontation with gold standard histologic techniques or immunohistochemistry to visualize type II collagen. This system permitted to differentiate the quality of collagen network between ITS and TGF-β1 treatments. Multiscale large field imaging combined to multimodality approaches (SHG-TCSPC) at macroscopical scale represent an innovative and non-invasive technique to monitor the quality of collagen network in cartilage tissue-engineered substitutes before in vivo implantation.

Delay and Disruption Tolerant Networking MACHETE Model

NASA Technical Reports Server (NTRS)

Segui, John S.; Jennings, Esther H.; Gao, Jay L.

2011-01-01

To verify satisfaction of communication requirements imposed by unique missions, as early as 2000, the Communications Networking Group at the Jet Propulsion Laboratory (JPL) saw the need for an environment to support interplanetary communication protocol design, validation, and characterization. JPL's Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE), described in Simulator of Space Communication Networks (NPO-41373) NASA Tech Briefs, Vol. 29, No. 8 (August 2005), p. 44, combines various commercial, non-commercial, and in-house custom tools for simulation and performance analysis of space networks. The MACHETE environment supports orbital analysis, link budget analysis, communications network simulations, and hardware-in-the-loop testing. As NASA is expanding its Space Communications and Navigation (SCaN) capabilities to support planned and future missions, building infrastructure to maintain services and developing enabling technologies, an important and broader role is seen for MACHETE in design-phase evaluation of future SCaN architectures. To support evaluation of the developing Delay Tolerant Networking (DTN) field and its applicability for space networks, JPL developed MACHETE models for DTN Bundle Protocol (BP) and Licklider/Long-haul Transmission Protocol (LTP). DTN is an Internet Research Task Force (IRTF) architecture providing communication in and/or through highly stressed networking environments such as space exploration and battlefield networks. Stressed networking environments include those with intermittent (predictable and unknown) connectivity, large and/or variable delays, and high bit error rates. To provide its services over existing domain specific protocols, the DTN protocols reside at the application layer of the TCP/IP stack, forming a store-and-forward overlay network. The key capabilities of the Bundle Protocol include custody-based reliability, the ability to cope with intermittent connectivity, the ability to take advantage of scheduled and opportunistic connectivity, and late binding of names to addresses.

MicroRadarNet: A network of weather micro radars for the identification of local high resolution precipitation patterns

NASA Astrophysics Data System (ADS)

Turso, S.; Paolella, S.; Gabella, M.; Perona, G.

2013-01-01

In this paper, MicroRadarNet, a novel micro radar network for continuous, unattended meteorological monitoring is presented. Key aspects and constraints are introduced. Specific design strategies are highlighted, leading to the technological implementations of this wireless, low-cost, low power consumption sensor network. Raw spatial and temporal datasets are processed on-board in real-time, featuring a consistent evaluation of the signals from the sensors and optimizing the data loads to be transmitted. Network servers perform the final post-elaboration steps on the data streams coming from each unit. Final network products are meteorological mappings of weather events, monitored with high spatial and temporal resolution, and lastly served to the end user through any Web browser. This networked approach is shown to imply a sensible reduction of the overall operational costs, including management and maintenance aspects, if compared to the traditional long range monitoring strategy. Adoption of the TITAN storm identification and nowcasting engine is also here evaluated for in-loop integration within the MicroRadarNet data processing chain. A brief description of the engine workflow is provided, to present preliminary feasibility results and performance estimates. The outcomes were not so predictable, taking into account relevant operational differences between a Western Alps micro radar scenario and the long range radar context in the Denver region of Colorado. Finally, positive results from a set of case studies are discussed, motivating further refinements and integration activities.

An analysis of image storage systems for scalable training of deep neural networks

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

Lim, Seung-Hwan; Young, Steven R; Patton, Robert M

This study presents a principled empirical evaluation of image storage systems for training deep neural networks. We employ the Caffe deep learning framework to train neural network models for three different data sets, MNIST, CIFAR-10, and ImageNet. While training the models, we evaluate five different options to retrieve training image data: (1) PNG-formatted image files on local file system; (2) pushing pixel arrays from image files into a single HDF5 file on local file system; (3) in-memory arrays to hold the pixel arrays in Python and C++; (4) loading the training data into LevelDB, a log-structured merge tree based key-valuemore » storage; and (5) loading the training data into LMDB, a B+tree based key-value storage. The experimental results quantitatively highlight the disadvantage of using normal image files on local file systems to train deep neural networks and demonstrate reliable performance with key-value storage based storage systems. When training a model on the ImageNet dataset, the image file option was more than 17 times slower than the key-value storage option. Along with measurements on training time, this study provides in-depth analysis on the cause of performance advantages/disadvantages of each back-end to train deep neural networks. We envision the provided measurements and analysis will shed light on the optimal way to architect systems for training neural networks in a scalable manner.« less

Evaluating the performance of free-formed surface parts using an analytic network process

NASA Astrophysics Data System (ADS)

Qian, Xueming; Ma, Yanqiao; Liang, Dezhi

2018-03-01

To successfully design parts with a free-formed surface, the critical issue of how to evaluate and select a favourable evaluation strategy before design is raised. The evaluation of free-formed surface parts is a multiple criteria decision-making (MCDM) problem that requires the consideration of a large number of interdependent factors. The analytic network process (ANP) is a relatively new MCDM method that can systematically deal with all kinds of dependences. In this paper, the factors, which come from the life-cycle and influence the design of free-formed surface parts, are proposed. After analysing the interdependence among these factors, a Hybrid ANP (HANP) structure for evaluating the part’s curved surface is constructed. Then, a HANP evaluation of an impeller is presented to illustrate the application of the proposed method.

The Use of Decentralized Control in the Design of a Large Segmented Space Reflector

NASA Technical Reports Server (NTRS)

Ryaciotaki-Boussalis, Helen; Mirmirani, Maj; Rad, Khosrow; Morales, Mauricio; Velazquez, Efrain; Chassiakos, Anastasios; Luzardo, Jose-Alberto

1997-01-01

The 3-dimensional model for a segmented reflector telescope is developed using finite element techniques. The structure is decomposed into six subsystems. System control design using neural networks is performed. Performance evaluation is demonstrated via simulation using PRO-MATLAB and SIMULINK.

QSAR modelling using combined simple competitive learning networks and RBF neural networks.

PubMed

Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

2018-04-01

The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

Performance assessment of Beijing Lightning Network (BLNET) and comparison with other lightning location networks across Beijing

NASA Astrophysics Data System (ADS)

Srivastava, Abhay; Tian, Ye; Qie, Xiushu; Wang, Dongfang; Sun, Zhuling; Yuan, Shanfeng; Wang, Yu; Chen, Zhixiong; Xu, Wenjing; Zhang, Hongbo; Jiang, Rubin; Su, Debin

2017-11-01

The performances of Beijing Lightning Network (BLNET) operated in Beijing-Tianjin-Hebei urban cluster area have been evaluated in terms of detection efficiency and relative location accuracy. A self-reference method has been used to show the detection efficiency of BLNET, for which fast antenna waveforms have been manually examined. Based on the fast antenna verification, the average detection efficiency of BLNET is 97.4% for intracloud (IC) flashes, 73.9% for cloud-to-ground (CG) flashes and 93.2% for the total flashes. Result suggests the CG detection of regional dense network is highly precise when the thunderstorm passes over the network; however it changes day to day when the thunderstorms are outside the network. Further, the CG stroke data from three different lightning location networks across Beijing are compared. The relative detection efficiency of World Wide Lightning Location Network (WWLLN) and Chinese Meteorology Administration - Lightning Detection Network (CMA-LDN, also known as ADTD) are approximately 12.4% (16.8%) and 36.5% (49.4%), respectively, comparing with fast antenna (BLNET). The location of BLNET is in middle, while WWLLN and CMA-LDN average locations are southeast and northwest, respectively. Finally, the IC pulses and CG return stroke pulses have been compared with the S-band Doppler radar. This type of study is useful to know the approximate situation in a region and improve the performance of lightning location networks in the absence of ground truth. Two lightning flashes occurred on tower in the coverage of BLNET show that the horizontal location error was 52.9 m and 250 m, respectively.

An adaptive density-based routing protocol for flying Ad Hoc networks

NASA Astrophysics Data System (ADS)

Zheng, Xueli; Qi, Qian; Wang, Qingwen; Li, Yongqiang

2017-10-01

An Adaptive Density-based Routing Protocol (ADRP) for Flying Ad Hoc Networks (FANETs) is proposed in this paper. The main objective is to calculate forwarding probability adaptively in order to increase the efficiency of forwarding in FANETs. ADRP dynamically fine-tunes the rebroadcasting probability of a node for routing request packets according to the number of neighbour nodes. Indeed, it is more interesting to privilege the retransmission by nodes with little neighbour nodes. We describe the protocol, implement it and evaluate its performance using NS-2 network simulator. Simulation results reveal that ADRP achieves better performance in terms of the packet delivery fraction, average end-to-end delay, normalized routing load, normalized MAC load and throughput, which is respectively compared with AODV.

Signal processing and neural network toolbox and its application to failure diagnosis and prognosis

NASA Astrophysics Data System (ADS)

Tu, Fang; Wen, Fang; Willett, Peter K.; Pattipati, Krishna R.; Jordan, Eric H.

2001-07-01

Many systems are comprised of components equipped with self-testing capability; however, if the system is complex involving feedback and the self-testing itself may occasionally be faulty, tracing faults to a single or multiple causes is difficult. Moreover, many sensors are incapable of reliable decision-making on their own. In such cases, a signal processing front-end that can match inference needs will be very helpful. The work is concerned with providing an object-oriented simulation environment for signal processing and neural network-based fault diagnosis and prognosis. In the toolbox, we implemented a wide range of spectral and statistical manipulation methods such as filters, harmonic analyzers, transient detectors, and multi-resolution decomposition to extract features for failure events from data collected by data sensors. Then we evaluated multiple learning paradigms for general classification, diagnosis and prognosis. The network models evaluated include Restricted Coulomb Energy (RCE) Neural Network, Learning Vector Quantization (LVQ), Decision Trees (C4.5), Fuzzy Adaptive Resonance Theory (FuzzyArtmap), Linear Discriminant Rule (LDR), Quadratic Discriminant Rule (QDR), Radial Basis Functions (RBF), Multiple Layer Perceptrons (MLP) and Single Layer Perceptrons (SLP). Validation techniques, such as N-fold cross-validation and bootstrap techniques, are employed for evaluating the robustness of network models. The trained networks are evaluated for their performance using test data on the basis of percent error rates obtained via cross-validation, time efficiency, generalization ability to unseen faults. Finally, the usage of neural networks for the prediction of residual life of turbine blades with thermal barrier coatings is described and the results are shown. The neural network toolbox has also been applied to fault diagnosis in mixed-signal circuits.

Development and field validation of a community-engaged particulate matter air quality monitoring network in Imperial, California, USA.

PubMed

Carvlin, Graeme N; Lugo, Humberto; Olmedo, Luis; Bejarano, Ester; Wilkie, Alexa; Meltzer, Dan; Wong, Michelle; King, Galatea; Northcross, Amanda; Jerrett, Michael; English, Paul B; Hammond, Donald; Seto, Edmund

2017-12-01

The Imperial County Community Air Monitoring Network was developed as part of a community-engaged research study to provide real-time particulate matter (PM) air quality information at a high spatial resolution in Imperial County, California. The network augmented the few existing regulatory monitors and increased monitoring near susceptible populations. Monitors were both calibrated and field validated, a key component of evaluating the quality of the data produced by the community monitoring network. This paper examines the performance of a customized version of the low-cost Dylos optical particle counter used in the community air monitors compared with both PM 2.5 and PM 10 (particulate matter with aerodynamic diameters <2.5 and <10 μm, respectively) federal equivalent method (FEM) beta-attenuation monitors (BAMs) and federal reference method (FRM) gravimetric filters at a collocation site in the study area. A conversion equation was developed that estimates particle mass concentrations from the native Dylos particle counts, taking into account relative humidity. The R 2 for converted hourly averaged Dylos mass measurements versus a PM 2.5 BAM was 0.79 and that versus a PM 10 BAM was 0.78. The performance of the conversion equation was evaluated at six other sites with collocated PM 2.5 environmental beta-attenuation monitors (EBAMs) located throughout Imperial County. The agreement of the Dylos with the EBAMs was moderate to high (R 2 = 0.35-0.81). The performance of low-cost air quality sensors in community networks is currently not well documented. This paper provides a methodology for quantifying the performance of a next-generation Dylos PM sensor used in the Imperial County Community Air Monitoring Network. This air quality network provides data at a much finer spatial and temporal resolution than has previously been possible with government monitoring efforts. Once calibrated and validated, these high-resolution data may provide more information on susceptible populations, assist in the identification of air pollution hotspots, and increase community awareness of air pollution.

Performance Evaluation of Peer-to-Peer Progressive Download in Broadband Access Networks

NASA Astrophysics Data System (ADS)

Shibuya, Megumi; Ogishi, Tomohiko; Yamamoto, Shu

P2P (Peer-to-Peer) file sharing architectures have scalable and cost-effective features. Hence, the application of P2P architectures to media streaming is attractive and expected to be an alternative to the current video streaming using IP multicast or content delivery systems because the current systems require expensive network infrastructures and large scale centralized cache storage systems. In this paper, we investigate the P2P progressive download enabling Internet video streaming services. We demonstrated the capability of the P2P progressive download in both laboratory test network as well as in the Internet. Through the experiments, we clarified the contribution of the FTTH links to the P2P progressive download in the heterogeneous access networks consisting of FTTH and ADSL links. We analyzed the cause of some download performance degradation occurred in the experiment and discussed about the effective methods to provide the video streaming service using P2P progressive download in the current heterogeneous networks.

Experimental evaluation of the Continuous Risk Profile (CRP) approach to the current Caltrans methodology for high collision concentration location identification

DOT National Transportation Integrated Search

2012-03-31

This report evaluates the performance of Continuous Risk Profile (CRP) compared with the : Sliding Window Method (SWM) and Peak Searching (PS) methods. These three network : screening methods all require the same inputs: traffic collision data and Sa...

Experimental evaluation of the Continuous Risk Profile (CRP) approach to the current Caltrans methodology for high collision concentration location identification.

DOT National Transportation Integrated Search

2012-03-01

This report evaluates the performance of Continuous Risk Profile (CRP) compared with the : Sliding Window Method (SWM) and Peak Searching (PS) methods. These three network : screening methods all require the same inputs: traffic collision data and Sa...

Evaluating Student Perceptions of Course Delivery Platforms

ERIC Educational Resources Information Center

Bramorski, Tom; Madan, Manu S.

2016-01-01

In this paper we evaluate effectiveness of course delivery mode on three dimensions: values, networking opportunities and learning. While students and their future employers are two important customers for the business program, we focus on the perception of students regarding the effectiveness of course delivery mode on program performance. The…

Application of Lidar Data to the Performance Evaluations of CMAQ Model

EPA Science Inventory

The Tropospheric Ozone (O3) Lidar Network (TOLNet) provides time/height O3 measurements from near the surface to the top of the troposphere to describe in high-fidelity spatial-temporal distributions, which is uniquely useful to evaluate the temporal evolution of O3 profiles in a...

New recursive-least-squares algorithms for nonlinear active control of sound and vibration using neural networks.

PubMed

Bouchard, M

2001-01-01

In recent years, a few articles describing the use of neural networks for nonlinear active control of sound and vibration were published. Using a control structure with two multilayer feedforward neural networks (one as a nonlinear controller and one as a nonlinear plant model), steepest descent algorithms based on two distinct gradient approaches were introduced for the training of the controller network. The two gradient approaches were sometimes called the filtered-x approach and the adjoint approach. Some recursive-least-squares algorithms were also introduced, using the adjoint approach. In this paper, an heuristic procedure is introduced for the development of recursive-least-squares algorithms based on the filtered-x and the adjoint gradient approaches. This leads to the development of new recursive-least-squares algorithms for the training of the controller neural network in the two networks structure. These new algorithms produce a better convergence performance than previously published algorithms. Differences in the performance of algorithms using the filtered-x and the adjoint gradient approaches are discussed in the paper. The computational load of the algorithms discussed in the paper is evaluated for multichannel systems of nonlinear active control. Simulation results are presented to compare the convergence performance of the algorithms, showing the convergence gain provided by the new algorithms.

A method for independent component graph analysis of resting-state fMRI.

PubMed

Ribeiro de Paula, Demetrius; Ziegler, Erik; Abeyasinghe, Pubuditha M; Das, Tushar K; Cavaliere, Carlo; Aiello, Marco; Heine, Lizette; di Perri, Carol; Demertzi, Athena; Noirhomme, Quentin; Charland-Verville, Vanessa; Vanhaudenhuyse, Audrey; Stender, Johan; Gomez, Francisco; Tshibanda, Jean-Flory L; Laureys, Steven; Owen, Adrian M; Soddu, Andrea

2017-03-01

Independent component analysis (ICA) has been extensively used for reducing task-free BOLD fMRI recordings into spatial maps and their associated time-courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non-contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data. Here, we detail a graph building technique that allows these ICNs to be analyzed with graph theory. First, ICA was performed at the single-subject level in 15 healthy volunteers using a 3T MRI scanner. The identification of nine networks was performed by a multiple-template matching procedure and a subsequent component classification based on the network "neuronal" properties. Second, for each of the identified networks, the nodes were defined as 1,015 anatomically parcellated regions. Third, between-node functional connectivity was established by building edge weights for each networks. Group-level graph analysis was finally performed for each network and compared to the classical network. Network graph comparison between the classically constructed network and the nine networks showed significant differences in the auditory and visual medial networks with regard to the average degree and the number of edges, while the visual lateral network showed a significant difference in the small-worldness. This novel approach permits us to take advantage of the well-recognized power of ICA in BOLD signal decomposition and, at the same time, to make use of well-established graph measures to evaluate connectivity differences. Moreover, by providing a graph for each separate network, it can offer the possibility to extract graph measures in a specific way for each network. This increased specificity could be relevant for studying pathological brain activity or altered states of consciousness as induced by anesthesia or sleep, where specific networks are known to be altered in different strength.

A distributed geo-routing algorithm for wireless sensor networks.

PubMed

Joshi, Gyanendra Prasad; Kim, Sung Won

2009-01-01

Geographic wireless sensor networks use position information for greedy routing. Greedy routing works well in dense networks, whereas in sparse networks it may fail and require a recovery algorithm. Recovery algorithms help the packet to get out of the communication void. However, these algorithms are generally costly for resource constrained position-based wireless sensor networks (WSNs). In this paper, we propose a void avoidance algorithm (VAA), a novel idea based on upgrading virtual distance. VAA allows wireless sensor nodes to remove all stuck nodes by transforming the routing graph and forwarding packets using only greedy routing. In VAA, the stuck node upgrades distance unless it finds a next hop node that is closer to the destination than it is. VAA guarantees packet delivery if there is a topologically valid path. Further, it is completely distributed, immediately responds to node failure or topology changes and does not require planarization of the network. NS-2 is used to evaluate the performance and correctness of VAA and we compare its performance to other protocols. Simulations show our proposed algorithm consumes less energy, has an efficient path and substantially less control overheads.

Rapid identifying high-influence nodes in complex networks

NASA Astrophysics Data System (ADS)

Song, Bo; Jiang, Guo-Ping; Song, Yu-Rong; Xia, Ling-Ling

2015-10-01

A tiny fraction of influential individuals play a critical role in the dynamics on complex systems. Identifying the influential nodes in complex networks has theoretical and practical significance. Considering the uncertainties of network scale and topology, and the timeliness of dynamic behaviors in real networks, we propose a rapid identifying method (RIM) to find the fraction of high-influential nodes. Instead of ranking all nodes, our method only aims at ranking a small number of nodes in network. We set the high-influential nodes as initial spreaders, and evaluate the performance of RIM by the susceptible-infected-recovered (SIR) model. The simulations show that in different networks, RIM performs well on rapid identifying high-influential nodes, which is verified by typical ranking methods, such as degree, closeness, betweenness, and eigenvector centrality methods. Project supported by the National Natural Science Foundation of China (Grant Nos. 61374180 and 61373136), the Ministry of Education Research in the Humanities and Social Sciences Planning Fund Project, China (Grant No. 12YJAZH120), and the Six Projects Sponsoring Talent Summits of Jiangsu Province, China (Grant No. RLD201212).

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.

Leaking privacy and shadow profiles in online social networks.

PubMed

Garcia, David

2017-08-01

Social interaction and data integration in the digital society can affect the control that individuals have on their privacy. Social networking sites can access data from other services, including user contact lists where nonusers are listed too. Although most research on online privacy has focused on inference of personal information of users, this data integration poses the question of whether it is possible to predict personal information of nonusers. This article tests the shadow profile hypothesis, which postulates that the data given by the users of an online service predict personal information of nonusers. Using data from a disappeared social networking site, we perform a historical audit to evaluate whether personal data of nonusers could have been predicted with the personal data and contact lists shared by the users of the site. We analyze personal information of sexual orientation and relationship status, which follow regular mixing patterns in the social network. Going back in time over the growth of the network, we measure predictor performance as a function of network size and tendency of users to disclose their contact lists. This article presents robust evidence supporting the shadow profile hypothesis and reveals a multiplicative effect of network size and disclosure tendencies that accelerates the performance of predictors. These results call for new privacy paradigms that take into account the fact that individual privacy decisions do not happen in isolation and are mediated by the decisions of others.

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

Dorier, Matthieu; Mubarak, Misbah; Ross, Rob

Two-tiered direct network topologies such as Dragonflies have been proposed for future post-petascale and exascale machines, since they provide a high-radix, low-diameter, fast interconnection network. Such topologies call for redesigning MPI collective communication algorithms in order to attain the best performance. Yet as increasingly more applications share a machine, it is not clear how these topology-aware algorithms will react to interference with concurrent jobs accessing the same network. In this paper, we study three topology-aware broadcast algorithms, including one designed by ourselves. We evaluate their performance through event-driven simulation for small- and large-sized broadcasts (in terms of both data sizemore » and number of processes). We study the effect of different routing mechanisms on the topology-aware collective algorithms, as well as their sensitivity to network contention with other jobs. Our results show that while topology-aware algorithms dramatically reduce link utilization, their advantage in terms of latency is more limited.« less

Network-based simulation of aircraft at gates in airport terminals

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

Cheng, Y.

1998-03-01

Simulation is becoming an essential tool for planning, design, and management of airport facilities. A simulation of aircraft at gates at an airport can be applied for various periodically performed applications, relating to the dynamic behavior of aircraft at gates in airport terminals for analyses, evaluations, and decision supports. Conventionally, such simulations are implemented using an event-driven method. For a more efficient simulation, this paper proposes a network-based method. The basic idea is to transform all the sequence constraint relations of aircraft at gates into a network. The simulation is done by calculating the longest path to all the nodesmore » in the network. The effect of the algorithm of the proposed method has been examined by experiments, and the superiority of the proposed method over the event-driven method is revealed through comprehensive comparisons of their overall simulation performance.« less

Network Speech Systems Technology Program.

DTIC Science & Technology

1980-09-30

ognized that the lumped-speaker approximation could be extended even more generally to include cases of combined circuit-switched speech and packet...based on these tables. The first function is an im- portant element of the more general task of system control for a switched network, which in...programs are in preparation, as described below, for both steady-state evaluation and dynamic performance simulation of the algorithm in general

Versatile RED-based buffer management mechanism for the efficient support of internet traffic

NASA Astrophysics Data System (ADS)

Nelissen, Jordi; De Cnodder, Stefaan

1999-11-01

This paper presents an evaluation of various GFR (Guaranteed Frame Rate) implementation proposals. By means of extensive simulations performed in different network environments we compare two ATM Forum example implementations, namely the `simple FIFO-based GFR.2 implementation' and the `per-VC threshold and scheduling implementation'. The lessons learned from this study are as well applicable to non-ATM network technologies.

MOBS - A modular on-board switching system

NASA Astrophysics Data System (ADS)

Berner, W.; Grassmann, W.; Piontek, M.

The authors describe a multibeam satellite system that is designed for business services and for communications at a high bit rate. The repeater is regenerative with a modular onboard switching system. It acts not only as baseband switch but also as the central node of the network, performing network control and protocol evaluation. The hardware is based on a modular bus/memory architecture with associated processors.

Energy Efficient Medium Access Control Protocol for Clustered Wireless Sensor Networks with Adaptive Cross-Layer Scheduling.

PubMed

Sefuba, Maria; Walingo, Tom; Takawira, Fambirai

2015-09-18

This paper presents an Energy Efficient Medium Access Control (MAC) protocol for clustered wireless sensor networks that aims to improve energy efficiency and delay performance. The proposed protocol employs an adaptive cross-layer intra-cluster scheduling and an inter-cluster relay selection diversity. The scheduling is based on available data packets and remaining energy level of the source node (SN). This helps to minimize idle listening on nodes without data to transmit as well as reducing control packet overhead. The relay selection diversity is carried out between clusters, by the cluster head (CH), and the base station (BS). The diversity helps to improve network reliability and prolong the network lifetime. Relay selection is determined based on the communication distance, the remaining energy and the channel quality indicator (CQI) for the relay cluster head (RCH). An analytical framework for energy consumption and transmission delay for the proposed MAC protocol is presented in this work. The performance of the proposed MAC protocol is evaluated based on transmission delay, energy consumption, and network lifetime. The results obtained indicate that the proposed MAC protocol provides improved performance than traditional cluster based MAC protocols.

Cascaded clocks measurement and simulation findings

NASA Technical Reports Server (NTRS)

Chislow, Don; Zampetti, George

1994-01-01

This paper will examine aspects related to network synchronization distribution and the cascading of timing elements. Methods of timing distribution have become a much debated topic in standards forums and among network service providers (both domestically and internationally). Essentially these concerns focus on the need to migrate their existing network synchronization plans (and capabilities) to those required for the next generation of transport technologies (namely, the Synchronous Digital Hierarchy (SDH), Synchronous Optical Networks (SONET), and Asynchronous Transfer Mode (ATM). The particular choices for synchronization distribution network architectures are now being evaluated and are demonstrating that they can indeed have a profound effect on the overall service performance levels that will be delivered to the customer. The salient aspects of these concerns reduce to the following: (1) identifying that the devil is in the details of the timing element specifications and the distribution of timing information (i.e., small design choices can have a large performance impact); (2) developing a standardized method of performance verification that will yield unambiguous results; and (3) presentation of those results. Specifically, this will be done for two general cases: an ideal input, and a noisy input to a cascaded chain of slave clocks.

A Lifetime Maximization Relay Selection Scheme in Wireless Body Area Networks.

PubMed

Zhang, Yu; Zhang, Bing; Zhang, Shi

2017-06-02

Network Lifetime is one of the most important metrics in Wireless Body Area Networks (WBANs). In this paper, a relay selection scheme is proposed under the topology constrains specified in the IEEE 802.15.6 standard to maximize the lifetime of WBANs through formulating and solving an optimization problem where relay selection of each node acts as optimization variable. Considering the diversity of the sensor nodes in WBANs, the optimization problem takes not only energy consumption rate but also energy difference among sensor nodes into account to improve the network lifetime performance. Since it is Non-deterministic Polynomial-hard (NP-hard) and intractable, a heuristic solution is then designed to rapidly address the optimization. The simulation results indicate that the proposed relay selection scheme has better performance in network lifetime compared with existing algorithms and that the heuristic solution has low time complexity with only a negligible performance degradation gap from optimal value. Furthermore, we also conduct simulations based on a general WBAN model to comprehensively illustrate the advantages of the proposed algorithm. At the end of the evaluation, we validate the feasibility of our proposed scheme via an implementation discussion.

Spread Spectrum Based Energy Efficient Collaborative Communication in Wireless Sensor Networks.

PubMed

Ghani, Anwar; Naqvi, Husnain; Sher, Muhammad; Khan, Muazzam Ali; Khan, Imran; Irshad, Azeem

2016-01-01

Wireless sensor networks consist of resource limited devices. Most crucial of these resources is battery life, as in most applications like battle field or volcanic area monitoring, it is often impossible to replace or recharge the power source. This article presents an energy efficient collaborative communication system based on spread spectrum to achieve energy efficiency as well as immunity against jamming, natural interference, noise suppression and universal frequency reuse. Performance of the proposed system is evaluated using the received signal power, bit error rate (BER) and energy consumption. The results show a direct proportionality between the power gain and the number of collaborative nodes as well as BER and signal-to-noise ratio (Eb/N0). The analytical and simulation results of the proposed system are compared with SISO system. The comparison reveals that SISO perform better than collaborative communication in case of small distances whereas collaborative communication performs better than SISO in case of long distances. On the basis of these results it is safe to conclude that collaborative communication in wireless sensor networks using wideband systems improves the life time of nodes in the networks thereby prolonging the network's life time.

Energy Efficient Medium Access Control Protocol for Clustered Wireless Sensor Networks with Adaptive Cross-Layer Scheduling

PubMed Central

Sefuba, Maria; Walingo, Tom; Takawira, Fambirai

2015-01-01

This paper presents an Energy Efficient Medium Access Control (MAC) protocol for clustered wireless sensor networks that aims to improve energy efficiency and delay performance. The proposed protocol employs an adaptive cross-layer intra-cluster scheduling and an inter-cluster relay selection diversity. The scheduling is based on available data packets and remaining energy level of the source node (SN). This helps to minimize idle listening on nodes without data to transmit as well as reducing control packet overhead. The relay selection diversity is carried out between clusters, by the cluster head (CH), and the base station (BS). The diversity helps to improve network reliability and prolong the network lifetime. Relay selection is determined based on the communication distance, the remaining energy and the channel quality indicator (CQI) for the relay cluster head (RCH). An analytical framework for energy consumption and transmission delay for the proposed MAC protocol is presented in this work. The performance of the proposed MAC protocol is evaluated based on transmission delay, energy consumption, and network lifetime. The results obtained indicate that the proposed MAC protocol provides improved performance than traditional cluster based MAC protocols. PMID:26393608

Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

PubMed Central

Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

2016-01-01

A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006

Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network.

PubMed

Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

2016-01-08

A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator.

PubMed

Hannula, Manne; Huttunen, Kerttu; Koskelo, Jukka; Laitinen, Tomi; Leino, Tuomo

2008-01-01

In this study, the performances of artificial neural network (ANN) analysis and multilinear regression (MLR) model-based estimation of heart rate were compared in an evaluation of individual cognitive workload. The data comprised electrocardiography (ECG) measurements and an evaluation of cognitive load that induces psychophysiological stress (PPS), collected from 14 interceptor fighter pilots during complex simulated F/A-18 Hornet air battles. In our data, the mean absolute error of the ANN estimate was 11.4 as a visual analog scale score, being 13-23% better than the mean absolute error of the MLR model in the estimation of cognitive workload.

A statistical framework for evaluating neural networks to predict recurrent events in breast cancer

NASA Astrophysics Data System (ADS)

Gorunescu, Florin; Gorunescu, Marina; El-Darzi, Elia; Gorunescu, Smaranda

2010-07-01

Breast cancer is the second leading cause of cancer deaths in women today. Sometimes, breast cancer can return after primary treatment. A medical diagnosis of recurrent cancer is often a more challenging task than the initial one. In this paper, we investigate the potential contribution of neural networks (NNs) to support health professionals in diagnosing such events. The NN algorithms are tested and applied to two different datasets. An extensive statistical analysis has been performed to verify our experiments. The results show that a simple network structure for both the multi-layer perceptron and radial basis function can produce equally good results, not all attributes are needed to train these algorithms and, finally, the classification performances of all algorithms are statistically robust. Moreover, we have shown that the best performing algorithm will strongly depend on the features of the datasets, and hence, there is not necessarily a single best classifier.

Joint Transmit Antenna Selection and Power Allocation for ISDF Relaying Mobile-to-Mobile Sensor Networks

PubMed Central

Xu, Lingwei; Zhang, Hao; Gulliver, T. Aaron

2016-01-01

The outage probability (OP) performance of multiple-relay incremental-selective decode-and-forward (ISDF) relaying mobile-to-mobile (M2M) sensor networks with transmit antenna selection (TAS) over N-Nakagami fading channels is investigated. Exact closed-form OP expressions for both optimal and suboptimal TAS schemes are derived. The power allocation problem is formulated to determine the optimal division of transmit power between the broadcast and relay phases. The OP performance under different conditions is evaluated via numerical simulation to verify the analysis. These results show that the optimal TAS scheme has better OP performance than the suboptimal scheme. Further, the power allocation parameter has a significant influence on the OP performance. PMID:26907282

Energy-saving framework for passive optical networks with ONU sleep/doze mode.

PubMed

Van, Dung Pham; Valcarenghi, Luca; Dias, Maluge Pubuduni Imali; Kondepu, Koteswararao; Castoldi, Piero; Wong, Elaine

2015-02-09

This paper proposes an energy-saving passive optical network framework (ESPON) that aims to incorporate optical network unit (ONU) sleep/doze mode into dynamic bandwidth allocation (DBA) algorithms to reduce ONU energy consumption. In the ESPON, the optical line terminal (OLT) schedules both downstream (DS) and upstream (US) transmissions in the same slot in an online and dynamic fashion whereas the ONU enters sleep mode outside the slot. The ONU sleep time is maximized based on both DS and US traffic. Moreover, during the slot, the ONU might enter doze mode when only its transmitter is idle to further improve energy efficiency. The scheduling order of data transmission, control message exchange, sleep period, and doze period defines an energy-efficient scheme under the ESPON. Three schemes are designed and evaluated in an extensive FPGA-based evaluation. Results show that whilst all the schemes significantly save ONU energy for different evaluation scenarios, the scheduling order has great impact on their performance. In addition, the ESPON allows for a scheduling order that saves ONU energy independently of the network reach.

Performance Evaluation and Community Application of Low-Cost Sensors for Ozone and Nitrogen Dioxide

EPA Science Inventory

This study reports on the performance of electrochemical-based low-cost sensors and their use in a community application. CairClip sensors were collocated with federal reference and equivalent methods and operated in a network of sites by citizen scientists (community members) in...

Six degree of freedom active vibration damping for space application

NASA Technical Reports Server (NTRS)

Haynes, Leonard S.

1993-01-01

Work performed during the period 1 Jan. - 31 Mar. 1993 on six degree of freedom active vibration damping for space application is presented. A performance and cost report is included. Topics covered include: actuator testing; mechanical amplifier design; and neural network control system development and experimental evaluation.

HPNAIDM: The High-Performance Network Anomaly/Intrusion Detection and Mitigation System

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

Chen, Yan

Identifying traffic anomalies and attacks rapidly and accurately is critical for large network operators. With the rapid growth of network bandwidth, such as the next generation DOE UltraScience Network, and fast emergence of new attacks/virus/worms, existing network intrusion detection systems (IDS) are insufficient because they: • Are mostly host-based and not scalable to high-performance networks; • Are mostly signature-based and unable to adaptively recognize flow-level unknown attacks; • Cannot differentiate malicious events from the unintentional anomalies. To address these challenges, we proposed and developed a new paradigm called high-performance network anomaly/intrustion detection and mitigation (HPNAIDM) system. The new paradigm ismore » significantly different from existing IDSes with the following features (research thrusts). • Online traffic recording and analysis on high-speed networks; • Online adaptive flow-level anomaly/intrusion detection and mitigation; • Integrated approach for false positive reduction. Our research prototype and evaluation demonstrate that the HPNAIDM system is highly effective and economically feasible. Beyond satisfying the pre-set goals, we even exceed that significantly (see more details in the next section). Overall, our project harvested 23 publications (2 book chapters, 6 journal papers and 15 peer-reviewed conference/workshop papers). Besides, we built a website for technique dissemination, which hosts two system prototype release to the research community. We also filed a patent application and developed strong international and domestic collaborations which span both academia and industry.« less

Collective leadership and safety cultures (Co-Lead): protocol for a mixed-methods pilot evaluation of the impact of a co-designed collective leadership intervention on team performance and safety culture in a hospital group in Ireland.

PubMed

McAuliffe, Eilish; De Brún, Aoife; Ward, Marie; O'Shea, Marie; Cunningham, Una; O'Donovan, Róisín; McGinley, Sinead; Fitzsimons, John; Corrigan, Siobhán; McDonald, Nick

2017-11-03

There is accumulating evidence implicating the role of leadership in system failures that have resulted in a range of errors in healthcare, from misdiagnoses to failures to recognise and respond to patient deterioration. This has led to concerns about traditional hierarchical leadership structures and created an interest in the development of collective ways of working that distribute leadership roles and responsibilities across team members. Such collective leadership approaches have been associated with improved team performance and staff engagement. This research seeks to improve our understanding of collective leadership by addressing two specific issues: (1) Does collective leadership emerge organically (and in what forms) in a newly networked structure? and (2) Is it possible to design and implement collective leadership interventions that enable teams to collectively improve team performance and patient safety? The first phase will include a social network analysis, using an online survey and semistructured interviews at three time points over 12 months, to document the frequency of contact and collaboration between senior hospital management staff in a recently configured hospital group. This study will explore how the network of 11 hospitals is operating and will assess whether collective leadership emerges organically. Second, collective leadership interventions will be co-designed during a series of workshops with healthcare staff, researchers and patient representatives, and then implemented and evaluated with four healthcare teams within the hospital network. A mixed-methods evaluation will explore the impact of the intervention on team effectiveness and team performance indicators to assess whether the intervention is suitable for wider roll-out and evaluation across the hospital group. Favourable ethical opinion has been received from the University College Dublin Research Ethics Committee (HREC-LS-16-116397/LS-16-20). Results will be disseminated via publication in peer-reviewed journals, national and international conferences, and to relevant stakeholders and interest groups. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

A Health Insurance Portability and Accountability Act–Compliant Ocular Telehealth Network for the Remote Diagnosis and Management of Diabetic Retinopathy

PubMed Central

Li, Yaqin; Karnowski, Thomas P.; Tobin, Kenneth W.; Giancardo, Luca; Morris, Scott; Sparrow, Sylvia E.; Garg, Seema; Fox, Karen

2011-01-01

Abstract In this article, we present the design and implementation of a regional ocular telehealth network for remote assessment and management of diabetic retinopathy (DR), including the design requirements, network topology, protocol design, system work flow, graphics user interfaces, and performance evaluation. The Telemedical Retinal Image Analysis and Diagnosis Network is a computer-aided, image analysis telehealth paradigm for the diagnosis of DR and other retinal diseases using fundus images acquired from primary care end users delivering care to underserved patient populations in the mid-South and southeastern United States. PMID:21819244

Comparison between extreme learning machine and wavelet neural networks in data classification

NASA Astrophysics Data System (ADS)

Yahia, Siwar; Said, Salwa; Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri

2017-03-01

Extreme learning Machine is a well known learning algorithm in the field of machine learning. It's about a feed forward neural network with a single-hidden layer. It is an extremely fast learning algorithm with good generalization performance. In this paper, we aim to compare the Extreme learning Machine with wavelet neural networks, which is a very used algorithm. We have used six benchmark data sets to evaluate each technique. These datasets Including Wisconsin Breast Cancer, Glass Identification, Ionosphere, Pima Indians Diabetes, Wine Recognition and Iris Plant. Experimental results have shown that both extreme learning machine and wavelet neural networks have reached good results.

Salience and Default Mode Network Coupling Predicts Cognition in Aging and Parkinson's Disease.

PubMed

Putcha, Deepti; Ross, Robert S; Cronin-Golomb, Alice; Janes, Amy C; Stern, Chantal E

2016-02-01

Cognitive impairment is common in Parkinson's disease (PD). Three neurocognitive networks support efficient cognition: the salience network, the default mode network, and the central executive network. The salience network is thought to switch between activating and deactivating the default mode and central executive networks. Anti-correlated interactions between the salience and default mode networks in particular are necessary for efficient cognition. Our previous work demonstrated altered functional coupling between the neurocognitive networks in non-demented individuals with PD compared to age-matched control participants. Here, we aim to identify associations between cognition and functional coupling between these neurocognitive networks in the same group of participants. We investigated the extent to which intrinsic functional coupling among these neurocognitive networks is related to cognitive performance across three neuropsychological domains: executive functioning, psychomotor speed, and verbal memory. Twenty-four non-demented individuals with mild to moderate PD and 20 control participants were scanned at rest and evaluated on three neuropsychological domains. PD participants were impaired on tests from all three domains compared to control participants. Our imaging results demonstrated that successful cognition across healthy aging and Parkinson's disease participants was related to anti-correlated coupling between the salience and default mode networks. Individuals with poorer performance scores across groups demonstrated more positive salience network/default-mode network coupling. Successful cognition relies on healthy coupling between the salience and default mode networks, which may become dysfunctional in PD. These results can help inform non-pharmacological interventions (repetitive transcranial magnetic stimulation) targeting these specific networks before they become vulnerable in early stages of Parkinson's disease.

Software Defined Networking challenges and future direction: A case study of implementing SDN features on OpenStack private cloud

NASA Astrophysics Data System (ADS)

Shamugam, Veeramani; Murray, I.; Leong, J. A.; Sidhu, Amandeep S.

2016-03-01

Cloud computing provides services on demand instantly, such as access to network infrastructure consisting of computing hardware, operating systems, network storage, database and applications. Network usage and demands are growing at a very fast rate and to meet the current requirements, there is a need for automatic infrastructure scaling. Traditional networks are difficult to automate because of the distributed nature of their decision making process for switching or routing which are collocated on the same device. Managing complex environments using traditional networks is time-consuming and expensive, especially in the case of generating virtual machines, migration and network configuration. To mitigate the challenges, network operations require efficient, flexible, agile and scalable software defined networks (SDN). This paper discuss various issues in SDN and suggests how to mitigate the network management related issues. A private cloud prototype test bed was setup to implement the SDN on the OpenStack platform to test and evaluate the various network performances provided by the various configurations.

Telecommuting (Work-At-Home) at NASA Lewis Research Center

NASA Technical Reports Server (NTRS)

Srinidhi, Saragur M.

1994-01-01

This report presents a study in evaluating the viability of providing a work-at-home (telecommuting) program for Lewis Research Center's corporate employees using Integrated Services Digital Network (ISDN). Case studies have been presented for a range of applications from casual data access to interactive access. The network performance of telemedia applications were studied against future requirements for such level of remote connectivity. Many of the popular ISDN devices were characterized for network and service functionality. A set of recommendations to develop a telecommuting policy have been proposed.

Evaluation of Knowledge Development in a Healthcare Setting

NASA Astrophysics Data System (ADS)

Schaffer, Scott P.

Healthcare organizations worldwide have recently increased efforts to improve performance, quality, and knowledge transfer using information and communication technologies. Evaluation of the effectiveness and quality of such efforts is challenging. A macro and micro-level system evaluation conducted with a 14000 member US hospital administrative services organization examined the appropriateness of a blended face-to-face and technology-enabled performance improvement and knowledge development system. Furthermore, a successful team or microsystem in a high performing hospital was studied in-depth. Several types of data methods including interview, observation, and questionnaire were used to address evaluation questions within a knowledge development framework created for the study. Results of this preliminary study focus on how this organization attempted to organize clinical improvement efforts around quality and performance improvement processes supported by networked technologies.

Speaker verification using committee neural networks.

PubMed

Reddy, Narender P; Buch, Ojas A

2003-10-01

Security is a major problem in web based access or remote access to data bases. In the present study, the technique of committee neural networks was developed for speech based speaker verification. Speech data from the designated speaker and several imposters were obtained. Several parameters were extracted in the time and frequency domains, and fed to neural networks. Several neural networks were trained and the five best performing networks were recruited into the committee. The committee decision was based on majority voting of the member networks. The committee opinion was evaluated with further testing data. The committee correctly identified the designated speaker in (50 out of 50) 100% of the cases and rejected imposters in (150 out of 150) 100% of the cases. The committee decision was not unanimous in majority of the cases tested.

Application of neural networks to prediction of advanced composite structures mechanical response and behavior

NASA Technical Reports Server (NTRS)

Cios, K. J.; Vary, A.; Berke, L.; Kautz, H. E.

1992-01-01

Two types of neural networks were used to evaluate acousto-ultrasonic (AU) data for material characterization and mechanical reponse prediction. The neural networks included a simple feedforward network (backpropagation) and a radial basis functions network. Comparisons of results in terms of accuracy and training time are given. Acousto-ultrasonic (AU) measurements were performed on a series of tensile specimens composed of eight laminated layers of continuous, SiC fiber reinforced Ti-15-3 matrix. The frequency spectrum was dominated by frequencies of longitudinal wave resonance through the thickness of the specimen at the sending transducer. The magnitude of the frequency spectrum of the AU signal was used for calculating a stress-wave factor based on integrating the spectral distribution function and used for comparison with neural networks results.

Experimental demonstration of software defined data center optical networks with Tbps end-to-end tunability

NASA Astrophysics Data System (ADS)

Zhao, Yongli; Zhang, Jie; Ji, Yuefeng; Li, Hui; Wang, Huitao; Ge, Chao

2015-10-01

The end-to-end tunability is important to provision elastic channel for the burst traffic of data center optical networks. Then, how to complete the end-to-end tunability based on elastic optical networks? Software defined networking (SDN) based end-to-end tunability solution is proposed for software defined data center optical networks, and the protocol extension and implementation procedure are designed accordingly. For the first time, the flexible grid all optical networks with Tbps end-to-end tunable transport and switch system have been online demonstrated for data center interconnection, which are controlled by OpenDayLight (ODL) based controller. The performance of the end-to-end tunable transport and switch system has been evaluated with wavelength number tuning, bit rate tuning, and transmit power tuning procedure.

An Implementation of Wireless Body Area Networks for Improving Priority Data Transmission Delay.

PubMed

Gündoğdu, Köksal; Çalhan, Ali

2016-03-01

The rapid growth of wireless sensor networks has enabled the human health monitoring of patients using body sensor nodes that gather and evaluate human body parameters and movements. This study describes both simulation model and implementation of a new traffic sensitive wireless body area network by using non-preemptive priority queue discipline. A wireless body area network implementation employing TDMA is designed with three different priorities of data traffics. Besides, a coordinator node having the non-preemptive priority queue is performed in this study. We have also developed, modeled and simulated example network scenarios by using the Riverbed Modeler simulation software with the purpose of verifying the implementation results. The simulation results obtained under various network load conditions are consistent with the implementation results.

Deep hierarchical attention network for video description

NASA Astrophysics Data System (ADS)

Li, Shuohao; Tang, Min; Zhang, Jun

2018-03-01

Pairing video to natural language description remains a challenge in computer vision and machine translation. Inspired by image description, which uses an encoder-decoder model for reducing visual scene into a single sentence, we propose a deep hierarchical attention network for video description. The proposed model uses convolutional neural network (CNN) and bidirectional LSTM network as encoders while a hierarchical attention network is used as the decoder. Compared to encoder-decoder models used in video description, the bidirectional LSTM network can capture the temporal structure among video frames. Moreover, the hierarchical attention network has an advantage over single-layer attention network on global context modeling. To make a fair comparison with other methods, we evaluate the proposed architecture with different types of CNN structures and decoders. Experimental results on the standard datasets show that our model has a more superior performance than the state-of-the-art techniques.

Rutgers University Subcontract B611610 Final Report

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

Soundarajan, Sucheta; Eliassi-Rad, Tina; Gallagher, Brian

Given an incomplete (i.e., partially-observed) network, which nodes should we actively probe in order to achieve the highest accuracy for a given network feature? For example, consider a cyber-network administrator who observes only a portion of the network at time t and wants to accurately identify the most important (e.g., highest PageRank) nodes in the complete network. She has a limited budget for probing the network. Of all the nodes she has observed, which should she probe in order to most accurately identify the important nodes? We propose a novel and scalable algorithm, MaxOutProbe, and evaluate it w.r.t. four networkmore » features (largest connected component, PageRank, core-periphery, and community detection), five network sampling strategies, and seven network datasets from different domains. Across a range of conditions, MaxOutProbe demonstrates consistently high performance relative to several baseline strategies« less

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

NASA Technical Reports Server (NTRS)

Burken, John J.

2007-01-01

This paper describes the performance of a simplified dynamic inversion controller with neural network supplementation. This 6 DOF (Degree-of-Freedom) simulation study focuses on the results with and without adaptation of neural networks using a simulation of the NASA modified F-15 which has canards. One area of interest is the performance of a simulated surface failure while attempting to minimize the inertial cross coupling effect of a [B] matrix failure (a control derivative anomaly associated with a jammed or missing control surface). Another area of interest and presented is simulated aerodynamic failures ([A] matrix) such as a canard failure. The controller uses explicit models to produce desired angular rate commands. The dynamic inversion calculates the necessary surface commands to achieve the desired rates. The simplified dynamic inversion uses approximate short period and roll axis dynamics. Initial results indicated that the transient response for a [B] matrix failure using a Neural Network (NN) improved the control behavior when compared to not using a neural network for a given failure, However, further evaluation of the controller was comparable, with objections io the cross coupling effects (after changes were made to the controller). This paper describes the methods employed to reduce the cross coupling effect and maintain adequate tracking errors. The IA] matrix failure results show that control of the aircraft without adaptation is more difficult [leas damped) than with active neural networks, Simulation results show Neural Network augmentation of the controller improves performance in terms of backing error and cross coupling reduction and improved performance with aerodynamic-type failures.

Unraveling Network-induced Memory Contention: Deeper Insights with Machine Learning

DOE PAGES

Groves, Taylor Liles; Grant, Ryan; Gonzales, Aaron; ...

2017-11-21

Remote Direct Memory Access (RDMA) is expected to be an integral communication mechanism for future exascale systems enabling asynchronous data transfers, so that applications may fully utilize CPU resources while simultaneously sharing data amongst remote nodes. We examine Network-induced Memory Contention (NiMC) on Infiniband networks. We expose the interactions between RDMA, main-memory and cache, when applications and out-of-band services compete for memory resources. We then explore NiMCs resulting impact on application-level performance. For a range of hardware technologies and HPC workloads, we quantify NiMC and show that NiMCs impact grows with scale resulting in up to 3X performance degradation atmore » scales as small as 8K processes even in applications that previously have been shown to be performance resilient in the presence of noise. In addition, this work examines the problem of predicting NiMC's impact on applications by leveraging machine learning and easily accessible performance counters. This approach provides additional insights about the root cause of NiMC and facilitates dynamic selection of potential solutions. Finally, we evaluated three potential techniques to reduce NiMCs impact, namely hardware offloading, core reservation and network throttling.« less

Neural Net-Based Redesign of Transonic Turbines for Improved Unsteady Aerodynamic Performance

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.; Rai, Man Mohan; Huber, Frank W.

1998-01-01

A recently developed neural net-based aerodynamic design procedure is used in the redesign of a transonic turbine stage to improve its unsteady aerodynamic performance. The redesign procedure used incorporates the advantages of both traditional response surface methodology (RSM) and neural networks by employing a strategy called parameter-based partitioning of the design space. Starting from the reference design, a sequence of response surfaces based on both neural networks and polynomial fits are constructed to traverse the design space in search of an optimal solution that exhibits improved unsteady performance. The procedure combines the power of neural networks and the economy of low-order polynomials (in terms of number of simulations required and network training requirements). A time-accurate, two-dimensional, Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the optimization procedure. The optimization procedure yields a modified design that improves the aerodynamic performance through small changes to the reference design geometry. The computed results demonstrate the capabilities of the neural net-based design procedure, and also show the tremendous advantages that can be gained by including high-fidelity unsteady simulations that capture the relevant flow physics in the design optimization process.

Unraveling Network-induced Memory Contention: Deeper Insights with Machine Learning

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

Groves, Taylor Liles; Grant, Ryan; Gonzales, Aaron

Remote Direct Memory Access (RDMA) is expected to be an integral communication mechanism for future exascale systems enabling asynchronous data transfers, so that applications may fully utilize CPU resources while simultaneously sharing data amongst remote nodes. We examine Network-induced Memory Contention (NiMC) on Infiniband networks. We expose the interactions between RDMA, main-memory and cache, when applications and out-of-band services compete for memory resources. We then explore NiMCs resulting impact on application-level performance. For a range of hardware technologies and HPC workloads, we quantify NiMC and show that NiMCs impact grows with scale resulting in up to 3X performance degradation atmore » scales as small as 8K processes even in applications that previously have been shown to be performance resilient in the presence of noise. In addition, this work examines the problem of predicting NiMC's impact on applications by leveraging machine learning and easily accessible performance counters. This approach provides additional insights about the root cause of NiMC and facilitates dynamic selection of potential solutions. Finally, we evaluated three potential techniques to reduce NiMCs impact, namely hardware offloading, core reservation and network throttling.« less

Application of Fuzzy-Logic Controller and Neural Networks Controller in Gas Turbine Speed Control and Overheating Control and Surge Control on Transient Performance

NASA Astrophysics Data System (ADS)

Torghabeh, A. A.; Tousi, A. M.

2007-08-01

This paper presents Fuzzy Logic and Neural Networks approach to Gas Turbine Fuel schedules. Modeling of non-linear system using feed forward artificial Neural Networks using data generated by a simulated gas turbine program is introduced. Two artificial Neural Networks are used , depicting the non-linear relationship between gas generator speed and fuel flow, and turbine inlet temperature and fuel flow respectively . Off-line fast simulations are used for engine controller design for turbojet engine based on repeated simulation. The Mamdani and Sugeno models are used to expression the Fuzzy system . The linguistic Fuzzy rules and membership functions are presents and a Fuzzy controller will be proposed to provide an Open-Loop control for the gas turbine engine during acceleration and deceleration . MATLAB Simulink was used to apply the Fuzzy Logic and Neural Networks analysis. Both systems were able to approximate functions characterizing the acceleration and deceleration schedules . Surge and Flame-out avoidance during acceleration and deceleration phases are then checked . Turbine Inlet Temperature also checked and controls by Neural Networks controller. This Fuzzy Logic and Neural Network Controllers output results are validated and evaluated by GSP software . The validation results are used to evaluate the generalization ability of these artificial Neural Networks and Fuzzy Logic controllers.

Software for Brain Network Simulations: A Comparative Study

PubMed Central

Tikidji-Hamburyan, Ruben A.; Narayana, Vikram; Bozkus, Zeki; El-Ghazawi, Tarek A.

2017-01-01

Numerical simulations of brain networks are a critical part of our efforts in understanding brain functions under pathological and normal conditions. For several decades, the community has developed many software packages and simulators to accelerate research in computational neuroscience. In this article, we select the three most popular simulators, as determined by the number of models in the ModelDB database, such as NEURON, GENESIS, and BRIAN, and perform an independent evaluation of these simulators. In addition, we study NEST, one of the lead simulators of the Human Brain Project. First, we study them based on one of the most important characteristics, the range of supported models. Our investigation reveals that brain network simulators may be biased toward supporting a specific set of models. However, all simulators tend to expand the supported range of models by providing a universal environment for the computational study of individual neurons and brain networks. Next, our investigations on the characteristics of computational architecture and efficiency indicate that all simulators compile the most computationally intensive procedures into binary code, with the aim of maximizing their computational performance. However, not all simulators provide the simplest method for module development and/or guarantee efficient binary code. Third, a study of their amenability for high-performance computing reveals that NEST can almost transparently map an existing model on a cluster or multicore computer, while NEURON requires code modification if the model developed for a single computer has to be mapped on a computational cluster. Interestingly, parallelization is the weakest characteristic of BRIAN, which provides no support for cluster computations and limited support for multicore computers. Fourth, we identify the level of user support and frequency of usage for all simulators. Finally, we carry out an evaluation using two case studies: a large network with simplified neural and synaptic models and a small network with detailed models. These two case studies allow us to avoid any bias toward a particular software package. The results indicate that BRIAN provides the most concise language for both cases considered. Furthermore, as expected, NEST mostly favors large network models, while NEURON is better suited for detailed models. Overall, the case studies reinforce our general observation that simulators have a bias in the computational performance toward specific types of the brain network models. PMID:28775687

Quantitative evaluation of an air-monitoring network using atmospheric transport modeling and frequency of detection methods

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

Rood, Arthur S.; Sondrup, A. Jeffrey; Ritter, Paul D.

A methodology to quantify the performance of an air monitoring network in terms of frequency of detection has been developed. The methodology utilizes an atmospheric transport model to predict air concentrations of radionuclides at the samplers for a given release time and duration. Frequency of detection is defined as the fraction of “events” that result in a detection at either a single sampler or network of samplers. An “event” is defined as a release of finite duration that begins on a given day and hour of the year from a facility with the potential to emit airborne radionuclides. Another metricmore » of interest is the network intensity, which is defined as the fraction of samplers in the network that have a positive detection for a given event. The frequency of detection methodology allows for evaluation of short-term releases that include effects of short-term variability in meteorological conditions. The methodology was tested using the U.S. Department of Energy Idaho National Laboratory (INL) Site ambient air monitoring network consisting of 37 low-volume air samplers in 31 different locations covering a 17,630 km 2 region. Releases from six major INL facilities distributed over an area of 1,435 km 2 were modeled and included three stack sources and eight ground-level sources. A Lagrangian Puff air dispersion model (CALPUFF) was used to model atmospheric transport. The model was validated using historical 125Sb releases and measurements. Relevant one-week release quantities from each emission source were calculated based on a dose of 1.9 × 10 –4 mSv at a public receptor (0.01 mSv assuming release persists over a year). Important radionuclides considered include 241Am, 137Cs, 238Pu, 239Pu, 90Sr, and tritium. Results show the detection frequency is over 97.5% for the entire network considering all sources and radionuclides. Network intensities ranged from 3.75% to 62.7%. Evaluation of individual samplers indicated some samplers were poorly situated and add little to the overall effectiveness of the network. As a result, using the frequency of detection methods, optimum sampler placements were simulated that could substantially improve the performance and efficiency of the network.« less

Quantitative evaluation of an air-monitoring network using atmospheric transport modeling and frequency of detection methods

DOE PAGES

Rood, Arthur S.; Sondrup, A. Jeffrey; Ritter, Paul D.

2016-04-01

A methodology to quantify the performance of an air monitoring network in terms of frequency of detection has been developed. The methodology utilizes an atmospheric transport model to predict air concentrations of radionuclides at the samplers for a given release time and duration. Frequency of detection is defined as the fraction of “events” that result in a detection at either a single sampler or network of samplers. An “event” is defined as a release of finite duration that begins on a given day and hour of the year from a facility with the potential to emit airborne radionuclides. Another metricmore » of interest is the network intensity, which is defined as the fraction of samplers in the network that have a positive detection for a given event. The frequency of detection methodology allows for evaluation of short-term releases that include effects of short-term variability in meteorological conditions. The methodology was tested using the U.S. Department of Energy Idaho National Laboratory (INL) Site ambient air monitoring network consisting of 37 low-volume air samplers in 31 different locations covering a 17,630 km 2 region. Releases from six major INL facilities distributed over an area of 1,435 km 2 were modeled and included three stack sources and eight ground-level sources. A Lagrangian Puff air dispersion model (CALPUFF) was used to model atmospheric transport. The model was validated using historical 125Sb releases and measurements. Relevant one-week release quantities from each emission source were calculated based on a dose of 1.9 × 10 –4 mSv at a public receptor (0.01 mSv assuming release persists over a year). Important radionuclides considered include 241Am, 137Cs, 238Pu, 239Pu, 90Sr, and tritium. Results show the detection frequency is over 97.5% for the entire network considering all sources and radionuclides. Network intensities ranged from 3.75% to 62.7%. Evaluation of individual samplers indicated some samplers were poorly situated and add little to the overall effectiveness of the network. As a result, using the frequency of detection methods, optimum sampler placements were simulated that could substantially improve the performance and efficiency of the network.« less

Experimental Verification of Electric Drive Technologies Based on Artificial Intelligence Tools

NASA Technical Reports Server (NTRS)

Rubaai, Ahmed; Ricketts, Daniel; Kotaru, Raj; Thomas, Robert; Noga, Donald F. (Technical Monitor); Kankam, Mark D. (Technical Monitor)

2000-01-01

In this report, a fully integrated prototype of a flight servo control system is successfully developed and implemented using brushless dc motors. The control system is developed by the fuzzy logic theory, and implemented with a multilayer neural network. First, a neural network-based architecture is introduced for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the neural network structure. The network structure and the parameter learning are performed simultaneously and online in the fuzzy-neural network system. The structure learning is based on the partition of input space. The parameter learning is based on the supervised gradient decent method, using a delta adaptation law. Using experimental setup, the performance of the proposed control system is evaluated under various operating conditions. Test results are presented and discussed in the report. The proposed learning control system has several advantages, namely, simple structure and learning capability, robustness and high tracking performance and few nodes at hidden layers. In comparison with the PI controller, the proposed fuzzy-neural network system can yield a better dynamic performance with shorter settling time, and without overshoot. Experimental results have shown that the proposed control system is adaptive and robust in responding to a wide range of operating conditions. In summary, the goal of this study is to design and implement-advanced servosystems to actuate control surfaces for flight vehicles, namely, aircraft and helicopters, missiles and interceptors, and mini- and micro-air vehicles.

Evaluation of Alternative Field Buses for Lighting ControlApplications

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

Koch, Ed; Rubinstein, Francis

2005-03-21

The Subcontract Statement of Work consists of two major tasks. This report is the Final Report in fulfillment of the contract deliverable for Task 1. The purpose of Task 1 was to evaluate existing and emerging protocols and standards for interfacing sensors and controllers for communicating with integrated lighting control systems in commercial buildings. The detailed task description follows: Task 1. Evaluate alternative sensor/field buses. The objective of this task is to evaluate existing and emerging standards for interfacing sensors and controllers for communicating with integrated lighting control systems in commercial buildings. The protocols to be evaluated will include atmore » least: (1) 1-Wire Net, (2) DALI, (3) MODBUS (or appropriate substitute such as EIB) and (4) ZigBee. The evaluation will include a comparative matrix for comparing the technical performance features of the different alternative systems. The performance features to be considered include: (1) directionality and network speed, (2) error control, (3) latency times, (4) allowable cable voltage drop, (5) topology, and (6) polarization. Specifically, Subcontractor will: (1) Analyze the proposed network architecture and identify potential problems that may require further research and specification. (2) Help identify and specify additional software and hardware components that may be required for the communications network to operate properly. (3) Identify areas of the architecture that can benefit from existing standards and technology and enumerate those standards and technologies. (4) Identify existing companies that may have relevant technology that can be applied to this research. (5) Help determine if new standards or technologies need to be developed.« less

Shortest path problem on a grid network with unordered intermediate points

NASA Astrophysics Data System (ADS)

Saw, Veekeong; Rahman, Amirah; Eng Ong, Wen

2017-10-01

We consider a shortest path problem with single cost factor on a grid network with unordered intermediate points. A two stage heuristic algorithm is proposed to find a feasible solution path within a reasonable amount of time. To evaluate the performance of the proposed algorithm, computational experiments are performed on grid maps of varying size and number of intermediate points. Preliminary results for the problem are reported. Numerical comparisons against brute forcing show that the proposed algorithm consistently yields solutions that are within 10% of the optimal solution and uses significantly less computation time.

A multi-objective assessment of an air quality monitoring network using environmental, economic, and social indicators and GIS-based models.

PubMed

Pope, Ronald; Wu, Jianguo

2014-06-01

In the United States, air pollution is primarily measured by Air Quality Monitoring Networks (AQMN). These AQMNs have multiple objectives, including characterizing pollution patterns, protecting the public health, and determining compliance with air quality standards. In 2006, the U.S. Environmental Protection Agency issued a directive that air pollution agencies assess the performance of their AQMNs. Although various methods to design and assess AQMNs exist, here we demonstrate a geographic information system (GIS)-based approach that combines environmental, economic, and social indicators through the assessment of the ozone (O3) and particulate matter (PM10) networks in Maricopa County, Arizona. The assessment was conducted in three phases: (1) to evaluate the performance of the existing networks, (2) to identify areas that would benefit from the addition of new monitoring stations, and (3) to recommend changes to the AQMN. A comprehensive set of indicators was created for evaluating differing aspects of the AQMNs' objectives, and weights were applied to emphasize important indicators. Indicators were also classified according to their sustainable development goal. Our results showed that O3 was well represented in the county with some redundancy in terms of the urban monitors. The addition of weights to the indicators only had a minimal effect on the results. For O3, urban monitors had greater social scores, while rural monitors had greater environmental scores. The results did not suggest a need for adding more O3 monitoring sites. For PM10, clustered urban monitors were redundant, and weights also had a minimal effect on the results. The clustered urban monitors had overall low scores; sites near point sources had high environmental scores. Several areas were identified as needing additional PM10 monitors. This study demonstrates the usefulness of a multi-indicator approach to assess AQMNs. Network managers and planners may use this method to assess the performance of air quality monitoring networks in urban regions. The U.S. Environmental Protection Agency issued a directive in 2006 that air pollution agencies assess the performance of their AQMNs; as a result, we developed a GIS-based, multi-objective assessment approach that integrates environmental, economic, and social indicators, and demonstrates its use through assessing the O3 and PM10 monitoring networks in the Phoenix metropolitan area. We exhibit a method of assessing network performance and identifying areas that would benefit from new monitoring stations; also, we demonstrate the effect of adding weights to the indicators. Our study shows that using a multi-indicator approach gave detailed assessment results for the Phoenix AQMN.

Development of a computational model on the neural activity patterns of a visual working memory in a hierarchical feedforward Network

NASA Astrophysics Data System (ADS)

An, Soyoung; Choi, Woochul; Paik, Se-Bum

2015-11-01

Understanding the mechanism of information processing in the human brain remains a unique challenge because the nonlinear interactions between the neurons in the network are extremely complex and because controlling every relevant parameter during an experiment is difficult. Therefore, a simulation using simplified computational models may be an effective approach. In the present study, we developed a general model of neural networks that can simulate nonlinear activity patterns in the hierarchical structure of a neural network system. To test our model, we first examined whether our simulation could match the previously-observed nonlinear features of neural activity patterns. Next, we performed a psychophysics experiment for a simple visual working memory task to evaluate whether the model could predict the performance of human subjects. Our studies show that the model is capable of reproducing the relationship between memory load and performance and may contribute, in part, to our understanding of how the structure of neural circuits can determine the nonlinear neural activity patterns in the human brain.

Rapid Calculation of Max-Min Fair Rates for Multi-Commodity Flows in Fat-Tree Networks

DOE PAGES

Mollah, Md Atiqul; Yuan, Xin; Pakin, Scott; ...

2017-08-29

Max-min fairness is often used in the performance modeling of interconnection networks. Existing methods to compute max-min fair rates for multi-commodity flows have high complexity and are computationally infeasible for large networks. In this paper, we show that by considering topological features, this problem can be solved efficiently for the fat-tree topology that is widely used in data centers and high performance compute clusters. Several efficient new algorithms are developed for this problem, including a parallel algorithm that can take advantage of multi-core and shared-memory architectures. Using these algorithms, we demonstrate that it is possible to find the max-min fairmore » rate allocation for multi-commodity flows in fat-tree networks that support tens of thousands of nodes. We evaluate the run-time performance of the proposed algorithms and show improvement in orders of magnitude over the previously best known method. Finally, we further demonstrate a new application of max-min fair rate allocation that is only computationally feasible using our new algorithms.« less

Performance of asynchronous transfer mode (ATM) local area and wide area networks for medical imaging transmission in clinical environment.

PubMed

Huang, H K; Wong, A W; Zhu, X

1997-01-01

Asynchronous transfer mode (ATM) technology emerges as a leading candidate for medical image transmission in both local area network (LAN) and wide area network (WAN) applications. This paper describes the performance of an ATM LAN and WAN network at the University of California, San Francisco. The measurements were obtained using an intensive care unit (ICU) server connecting to four image workstations (WS) at four different locations of a hospital-integrated picture archiving and communication system (HI-PACS) in a daily regular clinical environment. Four types of performance were evaluated: magnetic disk-to-disk, disk-to-redundant array of inexpensive disks (RAID), RAID-to-memory, and memory-to-memory. Results demonstrate that the transmission rate between two workstations can reach 5-6 Mbytes/s from RAID-to-memory, and 8-10 Mbytes/s from memory-to-memory. When the server has to send images to all four workstations simultaneously, the transmission rate to each WS is about 4 Mbytes/s. Both situations are adequate for radiologic image communications for picture archiving and communication systems (PACS) and teleradiology applications.

Rapid Calculation of Max-Min Fair Rates for Multi-Commodity Flows in Fat-Tree Networks

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

Mollah, Md Atiqul; Yuan, Xin; Pakin, Scott

Max-min fairness is often used in the performance modeling of interconnection networks. Existing methods to compute max-min fair rates for multi-commodity flows have high complexity and are computationally infeasible for large networks. In this paper, we show that by considering topological features, this problem can be solved efficiently for the fat-tree topology that is widely used in data centers and high performance compute clusters. Several efficient new algorithms are developed for this problem, including a parallel algorithm that can take advantage of multi-core and shared-memory architectures. Using these algorithms, we demonstrate that it is possible to find the max-min fairmore » rate allocation for multi-commodity flows in fat-tree networks that support tens of thousands of nodes. We evaluate the run-time performance of the proposed algorithms and show improvement in orders of magnitude over the previously best known method. Finally, we further demonstrate a new application of max-min fair rate allocation that is only computationally feasible using our new algorithms.« less

Telecommunication Platforms for Transmitting Sensor Data over Communication Networks—State of the Art and Challenges

PubMed Central

Staniec, Kamil; Habrych, Marcin

2016-01-01

The importance of constructing wide-area sensor networks for holistic environmental state evaluation has been demonstrated. A general structure of such a network has been presented with distinction of three segments: local (based on ZigBee, Ethernet and ModBus techniques), core (base on cellular technologies) and the storage/application. The implementation of these techniques requires knowledge of their technical limitations and electromagnetic compatibility issues. The former refer to ZigBee performance degradation in multi-hop transmission, whereas the latter are associated with the common electromagnetic spectrum sharing with other existing technologies or with undesired radiated emissions generated by the radio modules of the sensor network. In many cases, it is also necessary to provide a measurement station with autonomous energy source, such as solar. As stems from measurements of the energetic efficiency of these sources, one should apply them with care and perform detailed power budget since their real performance may turn out to be far from expected. This, in turn, may negatively affect—in particular—the operation of chemical sensors implemented in the network as they often require additional heating. PMID:27447633

A new BP Fourier algorithm and its application in English teaching evaluation

NASA Astrophysics Data System (ADS)

Pei, Xuehui; Pei, Guixin

2017-08-01

BP neural network algorithm has wide adaptability and accuracy when used in complicated system evaluation, but its calculation defects such as slow convergence have limited its practical application. The paper tries to speed up the calculation convergence of BP neural network algorithm with Fourier basis functions and presents a new BP Fourier algorithm for complicated system evaluation. First, shortages and working principle of BP algorithm are analyzed for subsequent targeted improvement; Second, the presented BP Fourier algorithm adopts Fourier basis functions to simplify calculation structure, designs new calculation transfer function between input and output layers, and conducts theoretical analysis to prove the efficiency of the presented algorithm; Finally, the presented algorithm is used in evaluating university English teaching and the application results shows that the presented BP Fourier algorithm has better performance in calculation efficiency and evaluation accuracy and can be used in evaluating complicated system practically.

Performance Evaluation of the NASA/KSC Transmission System

NASA Technical Reports Server (NTRS)

Christensen, Kenneth J.

2000-01-01

NASA-KSC currently uses three bridged 100-Mbps FDDI segments as its backbone for data traffic. The FDDI Transmission System (FTXS) connects the KSC industrial area, KSC launch complex 39 area, and the Cape Canaveral Air Force Station. The report presents a performance modeling study of the FTXS and the proposed ATM Transmission System (ATXS). The focus of the study is on performance of MPEG video transmission on these networks. Commercial modeling tools - the CACI Predictor and Comnet tools - were used. In addition, custom software tools were developed to characterize conversation pairs in Sniffer trace (capture) files to use as input to these tools. A baseline study of both non-launch and launch day data traffic on the FTXS is presented. MPEG-1 and MPEG-2 video traffic was characterized and the shaping of it evaluated. It is shown that the characteristics of a video stream has a direct effect on its performance in a network. It is also shown that shaping of video streams is necessary to prevent overflow losses and resulting poor video quality. The developed models can be used to predict when the existing FTXS will 'run out of room' and for optimizing the parameters of ATM links used for transmission of MPEG video. Future work with these models can provide useful input and validation to set-top box projects within the Advanced Networks Development group in NASA-KSC Development Engineering.

Performance Evaluation of Machine Learning Methods for Leaf Area Index Retrieval from Time-Series MODIS Reflectance Data

PubMed Central

Wang, Tongtong; Xiao, Zhiqiang; Liu, Zhigang

2017-01-01

Leaf area index (LAI) is an important biophysical parameter and the retrieval of LAI from remote sensing data is the only feasible method for generating LAI products at regional and global scales. However, most LAI retrieval methods use satellite observations at a specific time to retrieve LAI. Because of the impacts of clouds and aerosols, the LAI products generated by these methods are spatially incomplete and temporally discontinuous, and thus they cannot meet the needs of practical applications. To generate high-quality LAI products, four machine learning algorithms, including back-propagation neutral network (BPNN), radial basis function networks (RBFNs), general regression neutral networks (GRNNs), and multi-output support vector regression (MSVR) are proposed to retrieve LAI from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data in this study and performance of these machine learning algorithms is evaluated. The results demonstrated that GRNNs, RBFNs, and MSVR exhibited low sensitivity to training sample size, whereas BPNN had high sensitivity. The four algorithms performed slightly better with red, near infrared (NIR), and short wave infrared (SWIR) bands than red and NIR bands, and the results were significantly better than those obtained using single band reflectance data (red or NIR). Regardless of band composition, GRNNs performed better than the other three methods. Among the four algorithms, BPNN required the least training time, whereas MSVR needed the most for any sample size. PMID:28045443