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Sample records for performance vector network

  1. Video data compression using artificial neural network differential vector quantization

    NASA Technical Reports Server (NTRS)

    Krishnamurthy, Ashok K.; Bibyk, Steven B.; Ahalt, Stanley C.

    1991-01-01

    An artificial neural network vector quantizer is developed for use in data compression applications such as Digital Video. Differential Vector Quantization is used to preserve edge features, and a new adaptive algorithm, known as Frequency-Sensitive Competitive Learning, is used to develop the vector quantizer codebook. To develop real time performance, a custom Very Large Scale Integration Application Specific Integrated Circuit (VLSI ASIC) is being developed to realize the associative memory functions needed in the vector quantization algorithm. By using vector quantization, the need for Huffman coding can be eliminated, resulting in superior performance against channel bit errors than methods that use variable length codes.

  2. Predictive vector quantization using a neural network approach

    NASA Astrophysics Data System (ADS)

    Mohsenian, Nader; Rizvi, Syed A.; Nasrabadi, Nasser M.

    1993-07-01

    A new predictive vector quantization (PVQ) technique capable of exploring the nonlinear dependencies in addition to the linear dependencies that exist between adjacent blocks (vectors) of pixels is introduced. The two components of the PVQ scheme, the vector predictor and the vector quantizer, are implemented by two different classes of neural networks. A multilayer perceptron is used for the predictive component and Kohonen self- organizing feature maps are used to design the codebook for the vector quantizer. The multilayer perceptron uses the nonlinearity condition associated with its processing units to perform a nonlinear vector prediction. The second component of the PVQ scheme vector quantizers the residual vector that is formed by subtracting the output of the perceptron from the original input vector. The joint-optimization task of designing the two components of the PVQ scheme is also achieved. Simulation results are presented for still images with high visual quality.

  3. Distributed Estimation for Vector Signal in Linear Coherent Sensor Networks

    NASA Astrophysics Data System (ADS)

    Wu, Chien-Hsien; Lin, Ching-An

    We introduce the distributed estimation of a random vector signal in wireless sensor networks that follow coherent multiple access channel model. We adopt the linear minimum mean squared error fusion rule. The problem of interest is to design linear coding matrices for those sensors in the network so as to minimize mean squared error of the estimated vector signal under a total power constraint. We show that the problem can be formulated as a convex optimization problem and we obtain closed form expressions of the coding matrices. Numerical results are used to illustrate the performance of the proposed method.

  4. Fast modular network implementation for support vector machines.

    PubMed

    Huang, Guang-Bin; Mao, K Z; Siew, Chee-Kheong; Huang, De-Shuang

    2005-11-01

    Support vector machines (SVMs) have been extensively used. However, it is known that SVMs face difficulty in solving large complex problems due to the intensive computation involved in their training algorithms, which are at least quadratic with respect to the number of training examples. This paper proposes a new, simple, and efficient network architecture which consists of several SVMs each trained on a small subregion of the whole data sampling space and the same number of simple neural quantizer modules which inhibit the outputs of all the remote SVMs and only allow a single local SVM to fire (produce actual output) at any time. In principle, this region-computing based modular network method can significantly reduce the learning time of SVM algorithms without sacrificing much generalization performance. The experiments on a few real large complex benchmark problems demonstrate that our method can be significantly faster than single SVMs without losing much generalization performance.

  5. NASF transposition network: A computing network for unscrambling p-ordered vectors

    NASA Technical Reports Server (NTRS)

    Lim, R. S.

    1979-01-01

    The viewpoints of design, programming, and application of the transportation network (TN) is presented. The TN is a programmable combinational logic network that connects 521 memory modules to 512 processors. The unscrambling of p-ordered vectors to 1-ordered vectors in one cycle is described. The TN design is based upon the concept of cyclic groups from abstract algebra and primitive roots and indices from number theory. The programming of the TN is very simple, requiring only 20 bits: 10 bits for offset control and 10 bits for barrel switch shift control. This simple control is executed by the control unit (CU), not the processors. Any memory access by a processor must be coordinated with the CU and wait for all other processors to come to a synchronization point. These wait and synchronization events can be a degradation in performance to a computation. The TN application is for multidimensional data manipulation, matrix processing, and data sorting, and can also perform a perfect shuffle. Unlike other more complicated and powerful permutation networks, the TN cannot, if possible at all, unscramble non-p-ordered vectors in one cycle.

  6. A Distributed Support Vector Machine Learning Over Wireless Sensor Networks.

    PubMed

    Kim, Woojin; Stanković, Milos S; Johansson, Karl H; Kim, H Jin

    2015-11-01

    This paper is about fully-distributed support vector machine (SVM) learning over wireless sensor networks. With the concept of the geometric SVM, we propose to gossip the set of extreme points of the convex hull of local data set with neighboring nodes. It has the advantages of a simple communication mechanism and finite-time convergence to a common global solution. Furthermore, we analyze the scalability with respect to the amount of exchanged information and convergence time, with a specific emphasis on the small-world phenomenon. First, with the proposed naive convex hull algorithm, the message length remains bounded as the number of nodes increases. Second, by utilizing a small-world network, we have an opportunity to drastically improve the convergence performance with only a small increase in power consumption. These properties offer a great advantage when dealing with a large-scale network. Simulation and experimental results support the feasibility and effectiveness of the proposed gossip-based process and the analysis.

  7. Word Vectorization Using Relations among Words for Neural Network

    NASA Astrophysics Data System (ADS)

    Hotta, Hajime; Kittaka, Masanobu; Hagiwara, Masafumi

    In this paper, we propose a new vectorization method for a new generation of computational intelligence including neural networks and natural language processing. In recent years, various techniques of word vectorization have been proposed, many of which rely on the preparation of dictionaries. However, these techniques don't consider the symbol grounding problem for unknown types of data, which is one of the most fundamental issues on artificial intelligence. In order to avoid the symbol-grounding problem, pattern processing based methods, such as neural networks, are often used in various studies on self-directive systems and algorithms, and the merit of neural network is not exception in the natural language processing. The proposed method is a converter from one word input to one real-valued vector, whose algorithm is inspired by neural network architecture. The merits of the method are as follows: (1) the method requires no specific knowledge of linguistics e.g. word classes or grammatical one; (2) the method is a sequence learning technique and it can learn additional knowledge. The experiment showed the efficiency of word vectorization in terms of similarity measurement.

  8. Performance of the butterfly processor-memory interconnection in a vector environment

    NASA Astrophysics Data System (ADS)

    Brooks, E. D., III

    1985-02-01

    A fundamental hurdle impeding the development of large N common memory multiprocessors is the performance limitation in the switch connecting the processors to the memory modules. Multistage networks currently considered for this connection have a memory latency which grows like (ALPHA)log2N*. For scientific computing, it is natural to look for a multiprocessor architecture that will enable the use of vector operations to mask memory latency. The problem to be overcome here is the chaotic behavior introduced by conflicts occurring in the switch. The performance of the butterfly or indirect binary n-cube network in a vector processing environment is examined. A simple modification of the standard 2X2 switch node used in such networks which adaptively removes chaotic behavior during a vector operation is described.

  9. Demonstration of Cost-Effective, High-Performance Computing at Performance and Reliability Levels Equivalent to a 1994 Vector Supercomputer

    NASA Technical Reports Server (NTRS)

    Babrauckas, Theresa

    2000-01-01

    The Affordable High Performance Computing (AHPC) project demonstrated that high-performance computing based on a distributed network of computer workstations is a cost-effective alternative to vector supercomputers for running CPU and memory intensive design and analysis tools. The AHPC project created an integrated system called a Network Supercomputer. By connecting computer work-stations through a network and utilizing the workstations when they are idle, the resulting distributed-workstation environment has the same performance and reliability levels as the Cray C90 vector Supercomputer at less than 25 percent of the C90 cost. In fact, the cost comparison between a Cray C90 Supercomputer and Sun workstations showed that the number of distributed networked workstations equivalent to a C90 costs approximately 8 percent of the C90.

  10. Support vector machines (SVMs) for monitoring network design.

    PubMed

    Asefa, Tirusew; Kemblowski, Mariush; Urroz, Gilberto; McKee, Mac

    2005-01-01

    In this paper we present a hydrologic application of a new statistical learning methodology called support vector machines (SVMs). SVMs are based on minimization of a bound on the generalized error (risk) model, rather than just the mean square error over a training set. Due to Mercer's conditions on the kernels, the corresponding optimization problems are convex and hence have no local minima. In this paper, SVMs are illustratively used to reproduce the behavior of Monte Carlo-based flow and transport models that are in turn used in the design of a ground water contamination detection monitoring system. The traditional approach, which is based on solving transient transport equations for each new configuration of a conductivity field, is too time consuming in practical applications. Thus, there is a need to capture the behavior of the transport phenomenon in random media in a relatively simple manner. The objective of the exercise is to maximize the probability of detecting contaminants that exceed some regulatory standard before they reach a compliance boundary, while minimizing cost (i.e., number of monitoring wells). Application of the method at a generic site showed a rather promising performance, which leads us to believe that SVMs could be successfully employed in other areas of hydrology. The SVM was trained using 510 monitoring configuration samples generated from 200 Monte Carlo flow and transport realizations. The best configurations of well networks selected by the SVM were identical with the ones obtained from the physical model, but the reliabilities provided by the respective networks differ slightly.

  11. A viral protease relocalizes in the presence of the vector to promote vector performance

    PubMed Central

    Bak, Aurélie; Cheung, Andrea L.; Yang, Chunling; Whitham, Steven A.; Casteel, Clare L.

    2017-01-01

    Vector-borne pathogens influence host characteristics relevant to host–vector contact, increasing pathogen transmission and survival. Previously, we demonstrated that infection with Turnip mosaic virus, a member of one of the largest families of plant-infecting viruses, increases vector attraction and reproduction on infected hosts. These changes were due to a single viral protein, NIa-Pro. Here we show that NIa-Pro responds to the presence of the aphid vector during infection by relocalizing to the vacuole. Remarkably, vacuolar localization is required for NIa-Pro's ability to enhance aphid reproduction on host plants, vacuole localization disappears when aphids are removed, and this phenomenon occurs for another potyvirus, Potato virus Y, suggesting a conserved role for the protein in vector–host interactions. Taken together, these results suggest that potyviruses dynamically respond to the presence of their vectors, promoting insect performance and transmission only when needed. PMID:28205516

  12. Performance of Bayesian outlier diagnostic in testing mean vector

    NASA Astrophysics Data System (ADS)

    Mohammad, Rofizah; Hamzah, Firdaus Mohamad

    2014-09-01

    The diagnostic measure kd which is used to measure the effect of a single observation d on model choice was applied to a variety of univariate model. The purpose of this study is to assess the performance of this diagnostic measure when applying to multivariate structure for testing the specified mean vector. We illustrate the method using data generated from multivariate normal distribution. If X a p-variate normal random variable of size n with the mean vector θ and a known covariance matrix, we consider the null hypothesis that the mean vector θ is zero. From this simulation we test the performance of kd for several n and p values.

  13. Internal performance characteristics of thrust-vectored axisymmetric ejector nozzles

    NASA Technical Reports Server (NTRS)

    Lamb, Milton

    1995-01-01

    A series of thrust-vectored axisymmetric ejector nozzles were designed and experimentally tested for internal performance and pumping characteristics at the Langley research center. This study indicated that discontinuities in the performance occurred at low primary nozzle pressure ratios and that these discontinuities were mitigated by decreasing expansion area ratio. The addition of secondary flow increased the performance of the nozzles. The mid-to-high range of secondary flow provided the most overall improvements, and the greatest improvements were seen for the largest ejector area ratio. Thrust vectoring the ejector nozzles caused a reduction in performance and discharge coefficient. With or without secondary flow, the vectored ejector nozzles produced thrust vector angles that were equivalent to or greater than the geometric turning angle. With or without secondary flow, spacing ratio (ejector passage symmetry) had little effect on performance (gross thrust ratio), discharge coefficient, or thrust vector angle. For the unvectored ejectors, a small amount of secondary flow was sufficient to reduce the pressure levels on the shroud to provide cooling, but for the vectored ejector nozzles, a larger amount of secondary air was required to reduce the pressure levels to provide cooling.

  14. Modeling and performance analysis of GPS vector tracking algorithms

    NASA Astrophysics Data System (ADS)

    Lashley, Matthew

    This dissertation provides a detailed analysis of GPS vector tracking algorithms and the advantages they have over traditional receiver architectures. Standard GPS receivers use a decentralized architecture that separates the tasks of signal tracking and position/velocity estimation. Vector tracking algorithms combine the two tasks into a single algorithm. The signals from the various satellites are processed collectively through a Kalman filter. The advantages of vector tracking over traditional, scalar tracking methods are thoroughly investigated. A method for making a valid comparison between vector and scalar tracking loops is developed. This technique avoids the ambiguities encountered when attempting to make a valid comparison between tracking loops (which are characterized by noise bandwidths and loop order) and the Kalman filters (which are characterized by process and measurement noise covariance matrices) that are used by vector tracking algorithms. The improvement in performance offered by vector tracking is calculated in multiple different scenarios. Rule of thumb analysis techniques for scalar Frequency Lock Loops (FLL) are extended to the vector tracking case. The analysis tools provide a simple method for analyzing the performance of vector tracking loops. The analysis tools are verified using Monte Carlo simulations. Monte Carlo simulations are also used to study the effects of carrier to noise power density (C/N0) ratio estimation and the advantage offered by vector tracking over scalar tracking. The improvement from vector tracking ranges from 2.4 to 6.2 dB in various scenarios. The difference in the performance of the three vector tracking architectures is analyzed. The effects of using a federated architecture with and without information sharing between the receiver's channels are studied. A combination of covariance analysis and Monte Carlo simulation is used to analyze the performance of the three algorithms. The federated algorithm without

  15. Biologically relevant neural network architectures for support vector machines.

    PubMed

    Jändel, Magnus

    2014-01-01

    Neural network architectures that implement support vector machines (SVM) are investigated for the purpose of modeling perceptual one-shot learning in biological organisms. A family of SVM algorithms including variants of maximum margin, 1-norm, 2-norm and ν-SVM is considered. SVM training rules adapted for neural computation are derived. It is found that competitive queuing memory (CQM) is ideal for storing and retrieving support vectors. Several different CQM-based neural architectures are examined for each SVM algorithm. Although most of the sixty-four scanned architectures are unconvincing for biological modeling four feasible candidates are found. The seemingly complex learning rule of a full ν-SVM implementation finds a particularly simple and natural implementation in bisymmetric architectures. Since CQM-like neural structures are thought to encode skilled action sequences and bisymmetry is ubiquitous in motor systems it is speculated that trainable pattern recognition in low-level perception has evolved as an internalized motor programme.

  16. Fuzzy learning vector quantization neural network and its application for artificial odor recognition system

    NASA Astrophysics Data System (ADS)

    Kusumoputro, Benyamin; Budiarto, Hary; Jatmiko, Wisnu

    2000-03-01

    In this paper, a kind of fuzzy algorithm for learning vector quantization is developed and used as pattern classifiers with a supervised learning paradigm in artificial odor discrimination system. In this type of FLVQ, the neuron activation is derived through fuzziness of the input data, so that the neural system could deal with the statistical of the measurement error directly. During learning,the similarity between the training vector and the reference vectors are calculated, and the winning reference vector is updated in two ways. Firstly, by shifting the central position of the fuzzy reference vector toward or away from the input vector, and secondly, by modifying its fuzziness. Two types of fuzziness modifications are used, i.e., a constant modification factor and a variable modification factor. This type of FLVQ is different in nature with FALVQ, and in this paper, the performance of FNLVQ network is compared with that of FALVQ in artificial odor recognition system. Experimental results show that both FALVQ and FNLVQ provided high recognition probability in determining various learn-category of odors, however, the FNLVQ neural system has the ability to recognize the unlearn-category of odor that could not recognized by FALVQ neural system.

  17. A high-performance FFT algorithm for vector supercomputers

    NASA Technical Reports Server (NTRS)

    Bailey, David H.

    1988-01-01

    Many traditional algorithms for computing the fast Fourier transform (FFT) on conventional computers are unacceptable for advanced vector and parallel computers because they involve nonunit, power-of-two memory strides. A practical technique for computing the FFT that avoids all such strides and appears to be near-optimal for a variety of current vector and parallel computers is presented. Performance results of a program based on this technique are given. Notable among these results is that a FORTRAN implementation of this algorithm on the CRAY-2 runs up to 77-percent faster than Cray's assembly-coded library routine.

  18. Performance evaluation of the SX-6 vector architecture forscientific computations

    SciTech Connect

    Oliker, Leonid; Canning, Andrew; Carter, Jonathan Carter; Shalf,John; Skinner, David; Ethier, Stephane; Biswas, Rupak; Djomehri,Jahed; Van der Wijngaart, Rob

    2005-01-01

    The growing gap between sustained and peak performance for scientific applications is a well-known problem in high performance computing. The recent development of parallel vector systems offers the potential to reduce this gap for many computational science codes and deliver a substantial increase in computing capabilities. This paper examines the intranode performance of the NEC SX-6 vector processor, and compares it against the cache-based IBMPower3 and Power4 superscalar architectures, across a number of key scientific computing areas. First, we present the performance of a microbenchmark suite that examines many low-level machine characteristics. Next, we study the behavior of the NAS Parallel Benchmarks. Finally, we evaluate the performance of several scientific computing codes. Overall results demonstrate that the SX-6 achieves high performance on a large fraction of our application suite and often significantly outperforms the cache-based architectures. However, certain classes of applications are not easily amenable to vectorization and would require extensive algorithm and implementation reengineering to utilize the SX-6 effectively.

  19. Folksonomical P2P File Sharing Networks Using Vectorized KANSEI Information as Search Tags

    NASA Astrophysics Data System (ADS)

    Ohnishi, Kei; Yoshida, Kaori; Oie, Yuji

    We present the concept of folksonomical peer-to-peer (P2P) file sharing networks that allow participants (peers) to freely assign structured search tags to files. These networks are similar to folksonomies in the present Web from the point of view that users assign search tags to information distributed over a network. As a concrete example, we consider an unstructured P2P network using vectorized Kansei (human sensitivity) information as structured search tags for file search. Vectorized Kansei information as search tags indicates what participants feel about their files and is assigned by the participant to each of their files. A search query also has the same form of search tags and indicates what participants want to feel about files that they will eventually obtain. A method that enables file search using vectorized Kansei information is the Kansei query-forwarding method, which probabilistically propagates a search query to peers that are likely to hold more files having search tags that are similar to the query. The similarity between the search query and the search tags is measured in terms of their dot product. The simulation experiments examine if the Kansei query-forwarding method can provide equal search performance for all peers in a network in which only the Kansei information and the tendency with respect to file collection are different among all of the peers. The simulation results show that the Kansei query forwarding method and a random-walk-based query forwarding method, for comparison, work effectively in different situations and are complementary. Furthermore, the Kansei query forwarding method is shown, through simulations, to be superior to or equal to the random-walk based one in terms of search speed.

  20. Biasing vector network analyzers using variable frequency and amplitude signals

    NASA Astrophysics Data System (ADS)

    Nobles, J. E.; Zagorodnii, V.; Hutchison, A.; Celinski, Z.

    2016-08-01

    We report the development of a test setup designed to provide a variable frequency biasing signal to a vector network analyzer (VNA). The test setup is currently used for the testing of liquid crystal (LC) based devices in the microwave region. The use of an AC bias for LC based devices minimizes the negative effects associated with ionic impurities in the media encountered with DC biasing. The test setup utilizes bias tees on the VNA test station to inject the bias signal. The square wave biasing signal is variable from 0.5 to 36.0 V peak-to-peak (VPP) with a frequency range of DC to 10 kHz. The test setup protects the VNA from transient processes, voltage spikes, and high-frequency leakage. Additionally, the signals to the VNA are fused to ½ amp and clipped to a maximum of 36 VPP based on bias tee limitations. This setup allows us to measure S-parameters as a function of both the voltage and the frequency of the applied bias signal.

  1. Maximizing sparse matrix vector product performance in MIMD computers

    SciTech Connect

    McLay, R.T.; Kohli, H.S.; Swift, S.L.; Carey, G.F.

    1994-12-31

    A considerable component of the computational effort involved in conjugate gradient solution of structured sparse matrix systems is expended during the Matrix-Vector Product (MVP), and hence it is the focus of most efforts at improving performance. Such efforts are hindered on MIMD machines due to constraints on memory, cache and speed of memory-cpu data transfer. This paper describes a strategy for maximizing the performance of the local computations associated with the MVP. The method focuses on single stride memory access, and the efficient use of cache by pre-loading it with data that is re-used while bypassing it for other data. The algorithm is designed to behave optimally for varying grid sizes and number of unknowns per gridpoint. Results from an assembly language implementation of the strategy on the iPSC/860 show a significant improvement over the performance using FORTRAN.

  2. Monthly evaporation forecasting using artificial neural networks and support vector machines

    NASA Astrophysics Data System (ADS)

    Tezel, Gulay; Buyukyildiz, Meral

    2016-04-01

    Evaporation is one of the most important components of the hydrological cycle, but is relatively difficult to estimate, due to its complexity, as it can be influenced by numerous factors. Estimation of evaporation is important for the design of reservoirs, especially in arid and semi-arid areas. Artificial neural network methods and support vector machines (SVM) are frequently utilized to estimate evaporation and other hydrological variables. In this study, usability of artificial neural networks (ANNs) (multilayer perceptron (MLP) and radial basis function network (RBFN)) and ɛ-support vector regression (SVR) artificial intelligence methods was investigated to estimate monthly pan evaporation. For this aim, temperature, relative humidity, wind speed, and precipitation data for the period 1972 to 2005 from Beysehir meteorology station were used as input variables while pan evaporation values were used as output. The Romanenko and Meyer method was also considered for the comparison. The results were compared with observed class A pan evaporation data. In MLP method, four different training algorithms, gradient descent with momentum and adaptive learning rule backpropagation (GDX), Levenberg-Marquardt (LVM), scaled conjugate gradient (SCG), and resilient backpropagation (RBP), were used. Also, ɛ-SVR model was used as SVR model. The models were designed via 10-fold cross-validation (CV); algorithm performance was assessed via mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2). According to the performance criteria, the ANN algorithms and ɛ-SVR had similar results. The ANNs and ɛ-SVR methods were found to perform better than the Romanenko and Meyer methods. Consequently, the best performance using the test data was obtained using SCG(4,2,2,1) with R 2 = 0.905.

  3. Locally connected neural network with improved feature vector

    NASA Technical Reports Server (NTRS)

    Thomas, Tyson (Inventor)

    2004-01-01

    A pattern recognizer which uses neuromorphs with a fixed amount of energy that is distributed among the elements. The distribution of the energy is used to form a histogram which is used as a feature vector.

  4. Distributed Vector Estimation for Power- and Bandwidth-Constrained Wireless Sensor Networks

    NASA Astrophysics Data System (ADS)

    Sani, Alireza; Vosoughi, Azadeh

    2016-08-01

    We consider distributed estimation of a Gaussian vector with a linear observation model in an inhomogeneous wireless sensor network, where a fusion center (FC) reconstructs the unknown vector, using a linear estimator. Sensors employ uniform multi-bit quantizers and binary PSK modulation, and communicate with the FC over orthogonal power- and bandwidth-constrained wireless channels. We study transmit power and quantization rate (measured in bits per sensor) allocation schemes that minimize mean-square error (MSE). In particular, we derive two closed-form upper bounds on the MSE, in terms of the optimization parameters and propose coupled and decoupled resource allocation schemes that minimize these bounds. We show that the bounds are good approximations of the simulated MSE and the performance of the proposed schemes approaches the clairvoyant centralized estimation when total transmit power or bandwidth is very large. We study how the power and rate allocation are dependent on sensors observation qualities and channel gains, as well as total transmit power and bandwidth constraints. Our simulations corroborate our analytical results and illustrate the superior performance of the proposed algorithms.

  5. Double Virus Vector Infection to the Prefrontal Network of the Macaque Brain

    PubMed Central

    Tanaka, Shingo; Koizumi, Masashi; Kikusui, Takefumi; Ichihara, Nobutsune; Kato, Shigeki; Kobayashi, Kazuto; Sakagami, Masamichi

    2015-01-01

    To precisely understand how higher cognitive functions are implemented in the prefrontal network of the brain, optogenetic and pharmacogenetic methods to manipulate the signal transmission of a specific neural pathway are required. The application of these methods, however, has been mostly restricted to animals other than the primate, which is the best animal model to investigate higher cognitive functions. In this study, we used a double viral vector infection method in the prefrontal network of the macaque brain. This enabled us to express specific constructs into specific neurons that constitute a target pathway without use of germline genetic manipulation. The double-infection technique utilizes two different virus vectors in two monosynaptically connected areas. One is a vector which can locally infect cell bodies of projection neurons (local vector) and the other can retrogradely infect from axon terminals of the same projection neurons (retrograde vector). The retrograde vector incorporates the sequence which encodes Cre recombinase and the local vector incorporates the “Cre-On” FLEX double-floxed sequence in which a reporter protein (mCherry) was encoded. mCherry thus came to be expressed only in doubly infected projection neurons with these vectors. We applied this method to two macaque monkeys and targeted two different pathways in the prefrontal network: The pathway from the lateral prefrontal cortex to the caudate nucleus and the pathway from the lateral prefrontal cortex to the frontal eye field. As a result, mCherry-positive cells were observed in the lateral prefrontal cortex in all of the four injected hemispheres, indicating that the double virus vector transfection is workable in the prefrontal network of the macaque brain. PMID:26193102

  6. Diagnosing Anomalous Network Performance with Confidence

    SciTech Connect

    Settlemyer, Bradley W; Hodson, Stephen W; Kuehn, Jeffery A; Poole, Stephen W

    2011-04-01

    Variability in network performance is a major obstacle in effectively analyzing the throughput of modern high performance computer systems. High performance interconnec- tion networks offer excellent best-case network latencies; how- ever, highly parallel applications running on parallel machines typically require consistently high levels of performance to adequately leverage the massive amounts of available computing power. Performance analysts have usually quantified network performance using traditional summary statistics that assume the observational data is sampled from a normal distribution. In our examinations of network performance, we have found this method of analysis often provides too little data to under- stand anomalous network performance. Our tool, Confidence, instead uses an empirically derived probability distribution to characterize network performance. In this paper we describe several instances where the Confidence toolkit allowed us to understand and diagnose network performance anomalies that we could not adequately explore with the simple summary statis- tics provided by traditional measurement tools. In particular, we examine a multi-modal performance scenario encountered with an Infiniband interconnection network and we explore the performance repeatability on the custom Cray SeaStar2 interconnection network after a set of software and driver updates.

  7. A diagram for evaluating multiple aspects of model performance in simulating vector fields

    NASA Astrophysics Data System (ADS)

    Xu, Zhongfeng; Hou, Zhaolu; Han, Ying; Guo, Weidong

    2016-12-01

    Vector quantities, e.g., vector winds, play an extremely important role in climate systems. The energy and water exchanges between different regions are strongly dominated by wind, which in turn shapes the regional climate. Thus, how well climate models can simulate vector fields directly affects model performance in reproducing the nature of a regional climate. This paper devises a new diagram, termed the vector field evaluation (VFE) diagram, which is a generalized Taylor diagram and able to provide a concise evaluation of model performance in simulating vector fields. The diagram can measure how well two vector fields match each other in terms of three statistical variables, i.e., the vector similarity coefficient, root mean square length (RMSL), and root mean square vector difference (RMSVD). Similar to the Taylor diagram, the VFE diagram is especially useful for evaluating climate models. The pattern similarity of two vector fields is measured by a vector similarity coefficient (VSC) that is defined by the arithmetic mean of the inner product of normalized vector pairs. Examples are provided, showing that VSC can identify how close one vector field resembles another. Note that VSC can only describe the pattern similarity, and it does not reflect the systematic difference in the mean vector length between two vector fields. To measure the vector length, RMSL is included in the diagram. The third variable, RMSVD, is used to identify the magnitude of the overall difference between two vector fields. Examples show that the VFE diagram can clearly illustrate the extent to which the overall RMSVD is attributed to the systematic difference in RMSL and how much is due to the poor pattern similarity.

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

    NASA Technical Reports Server (NTRS)

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

    1991-01-01

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

  9. HYBRID NEURAL NETWORK AND SUPPORT VECTOR MACHINE METHOD FOR OPTIMIZATION

    NASA Technical Reports Server (NTRS)

    Rai, Man Mohan (Inventor)

    2005-01-01

    System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

  10. Hybrid Neural Network and Support Vector Machine Method for Optimization

    NASA Technical Reports Server (NTRS)

    Rai, Man Mohan (Inventor)

    2007-01-01

    System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

  11. Radio to microwave dielectric characterisation of constitutive electromagnetic soil properties using vector network analyses

    NASA Astrophysics Data System (ADS)

    Schwing, M.; Wagner, N.; Karlovsek, J.; Chen, Z.; Williams, D. J.; Scheuermann, A.

    2016-04-01

    The knowledge of constitutive broadband electromagnetic (EM) properties of porous media such as soils and rocks is essential in the theoretical and numerical modeling of EM wave propagation in the subsurface. This paper presents an experimental and numerical study on the performance EM measuring instruments for broadband EM wave in the radio-microwave frequency range. 3-D numerical calculations of a specific sensor were carried out using the Ansys HFSS (high frequency structural simulator) to further evaluate the probe performance. In addition, six different sensors of varying design, application purpose, and operational frequency range, were tested on different calibration liquids and a sample of fine-grained soil over a frequency range of 1 MHz-40 GHz using four vector network analysers. The resulting dielectric spectrum of the soil was analysed and interpreted using a 3-term Cole-Cole model under consideration of a direct current conductivity contribution. Comparison of sensor performances on calibration materials and fine-grained soils showed consistency in the measured dielectric spectra at a frequency range from 100 MHz-2 GHz. By combining open-ended coaxial line and coaxial transmission line measurements, the observable frequency window could be extended to a truly broad frequency range of 1 MHz-40 GHz.

  12. Network Allocation Vector (NAV) Optimization for Underwater Handshaking-Based Protocols

    PubMed Central

    Cho, Junho; Shitiri, Ethungshan; Cho, Ho-Shin

    2016-01-01

    In this paper, we obtained the optimized network allocation vector (NAV) for underwater handshaking-based protocols, as inefficient determination of the NAV leads to unnecessarily long silent periods. We propose a scheme which determines the NAV by taking into account all possible propagation delays: propagation delay between a source and a destination; propagation delay between a source and the neighbors; and propagation delay between a destination and the neighbors. Such an approach effectively allows the NAV to be determined precisely equal to duration of a busy channel, and the silent period can be set commensurate to that duration. This allows for improvements in the performance of handshaking-based protocols, such as the carrier sense multiple access/collision avoidance (CSMA/CA) protocol, in terms of throughput and fairness. To evaluate the performance of the proposed scheme, performance comparisons were carried out through simulations with prior NAV setting methods. The simulation results show that the proposed scheme outperforms the other schemes in terms of throughput and fairness. PMID:28029122

  13. Network Allocation Vector (NAV) Optimization for Underwater Handshaking-Based Protocols.

    PubMed

    Cho, Junho; Shitiri, Ethungshan; Cho, Ho-Shin

    2016-12-24

    In this paper, we obtained the optimized network allocation vector (NAV) for underwater handshaking-based protocols, as inefficient determination of the NAV leads to unnecessarily long silent periods. We propose a scheme which determines the NAV by taking into account all possible propagation delays: propagation delay between a source and a destination; propagation delay between a source and the neighbors; and propagation delay between a destination and the neighbors. Such an approach effectively allows the NAV to be determined precisely equal to duration of a busy channel, and the silent period can be set commensurate to that duration. This allows for improvements in the performance of handshaking-based protocols, such as the carrier sense multiple access/collision avoidance (CSMA/CA) protocol, in terms of throughput and fairness. To evaluate the performance of the proposed scheme, performance comparisons were carried out through simulations with prior NAV setting methods. The simulation results show that the proposed scheme outperforms the other schemes in terms of throughput and fairness.

  14. Effects of internal yaw-vectoring devices on the static performance of a pitch-vectoring nonaxisymmetric convergent-divergent nozzle

    NASA Technical Reports Server (NTRS)

    Asbury, Scott C.

    1993-01-01

    An investigation was conducted in the static test facility of the Langley 16-Foot Transonic Tunnel to evaluate the internal performance of a nonaxisymmetric convergent divergent nozzle designed to have simultaneous pitch and yaw thrust vectoring capability. This concept utilized divergent flap deflection for thrust vectoring in the pitch plane and flow-turning deflectors installed within the divergent flaps for yaw thrust vectoring. Modifications consisting of reducing the sidewall length and deflecting the sidewall outboard were investigated as means to increase yaw-vectoring performance. This investigation studied the effects of multiaxis (pitch and yaw) thrust vectoring on nozzle internal performance characteristics. All tests were conducted with no external flow, and nozzle pressure ratio was varied from 2.0 to approximately 13.0. The results indicate that this nozzle concept can successfully generate multiaxis thrust vectoring. Deflection of the divergent flaps produced resultant pitch vector angles that, although dependent on nozzle pressure ratio, were nearly equal to the geometric pitch vector angle. Losses in resultant thrust due to pitch vectoring were small or negligible. The yaw deflectors produced resultant yaw vector angles up to 21 degrees that were controllable by varying yaw deflector rotation. However, yaw deflector rotation resulted in significant losses in thrust ratios and, in some cases, nozzle discharge coefficient. Either of the sidewall modifications generally reduced these losses and increased maximum resultant yaw vector angle. During multiaxis (simultaneous pitch and yaw) thrust vectoring, little or no cross coupling between the thrust vectoring processes was observed.

  15. The holographic neural network: Performance comparison with other neural networks

    NASA Astrophysics Data System (ADS)

    Klepko, Robert

    1991-10-01

    The artificial neural network shows promise for use in recognition of high resolution radar images of ships. The holographic neural network (HNN) promises a very large data storage capacity and excellent generalization capability, both of which can be achieved with only a few learning trials, unlike most neural networks which require on the order of thousands of learning trials. The HNN is specially designed for pattern association storage, and mathematically realizes the storage and retrieval mechanisms of holograms. The pattern recognition capability of the HNN was studied, and its performance was compared with five other commonly used neural networks: the Adaline, Hamming, bidirectional associative memory, recirculation, and back propagation networks. The patterns used for testing represented artificial high resolution radar images of ships, and appear as a two dimensional topology of peaks with various amplitudes. The performance comparisons showed that the HNN does not perform as well as the other neural networks when using the same test data. However, modification of the data to make it appear more Gaussian distributed, improved the performance of the network. The HNN performs best if the data is completely Gaussian distributed.

  16. Target Localization in Wireless Sensor Networks Using Online Semi-Supervised Support Vector Regression

    PubMed Central

    Yoo, Jaehyun; Kim, H. Jin

    2015-01-01

    Machine learning has been successfully used for target localization in wireless sensor networks (WSNs) due to its accurate and robust estimation against highly nonlinear and noisy sensor measurement. For efficient and adaptive learning, this paper introduces online semi-supervised support vector regression (OSS-SVR). The first advantage of the proposed algorithm is that, based on semi-supervised learning framework, it can reduce the requirement on the amount of the labeled training data, maintaining accurate estimation. Second, with an extension to online learning, the proposed OSS-SVR automatically tracks changes of the system to be learned, such as varied noise characteristics. We compare the proposed algorithm with semi-supervised manifold learning, an online Gaussian process and online semi-supervised colocalization. The algorithms are evaluated for estimating the unknown location of a mobile robot in a WSN. The experimental results show that the proposed algorithm is more accurate under the smaller amount of labeled training data and is robust to varying noise. Moreover, the suggested algorithm performs fast computation, maintaining the best localization performance in comparison with the other methods. PMID:26024420

  17. Artificial Astrocytes Improve Neural Network Performance

    PubMed Central

    Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-01-01

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

  18. Artificial astrocytes improve neural network performance.

    PubMed

    Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-04-19

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

  19. Internal performance of two nozzles utilizing gimbal concepts for thrust vectoring

    NASA Technical Reports Server (NTRS)

    Berrier, Bobby L.; Taylor, John G.

    1990-01-01

    The internal performance of an axisymmetric convergent-divergent nozzle and a nonaxisymmetric convergent-divergent nozzle, both of which utilized a gimbal type mechanism for thrust vectoring was evaluated in the Static Test Facility of the Langley 16-Foot Transonic Tunnel. The nonaxisymmetric nozzle used the gimbal concept for yaw thrust vectoring only; pitch thrust vectoring was accomplished by simultaneous deflection of the upper and lower divergent flaps. The model geometric parameters investigated were pitch vector angle for the axisymmetric nozzle and pitch vector angle, yaw vector angle, nozzle throat aspect ratio, and nozzle expansion ratio for the nonaxisymmetric nozzle. All tests were conducted with no external flow, and nozzle pressure ratio was varied from 2.0 to approximately 12.0.

  20. Static internal performance of a single expansion ramp nozzle with multiaxis thrust vectoring capability

    NASA Technical Reports Server (NTRS)

    Capone, Francis J.; Schirmer, Alberto W.

    1993-01-01

    An investigation was conducted at static conditions in order to determine the internal performance characteristics of a multiaxis thrust vectoring single expansion ramp nozzle. Yaw vectoring was achieved by deflecting yaw flaps in the nozzle sidewall into the nozzle exhaust flow. In order to eliminate any physical interference between the variable angle yaw flap deflected into the exhaust flow and the nozzle upper ramp and lower flap which were deflected for pitch vectoring, the downstream corners of both the nozzle ramp and lower flap were cut off to allow for up to 30 deg of yaw vectoring. The effects of nozzle upper ramp and lower flap cutout, yaw flap hinge line location and hinge inclination angle, sidewall containment, geometric pitch vector angle, and geometric yaw vector angle were studied. This investigation was conducted in the static-test facility of the Langley 16-Foot Transonic Tunnel at nozzle pressure ratios up to 8.0.

  1. High Performance Networks for High Impact Science

    SciTech Connect

    Scott, Mary A.; Bair, Raymond A.

    2003-02-13

    This workshop was the first major activity in developing a strategic plan for high-performance networking in the Office of Science. Held August 13 through 15, 2002, it brought together a selection of end users, especially representing the emerging, high-visibility initiatives, and network visionaries to identify opportunities and begin defining the path forward.

  2. Enhancing neural-network performance via assortativity

    SciTech Connect

    Franciscis, Sebastiano de; Johnson, Samuel; Torres, Joaquin J.

    2011-03-15

    The performance of attractor neural networks has been shown to depend crucially on the heterogeneity of the underlying topology. We take this analysis a step further by examining the effect of degree-degree correlations - assortativity - on neural-network behavior. We make use of a method recently put forward for studying correlated networks and dynamics thereon, both analytically and computationally, which is independent of how the topology may have evolved. We show how the robustness to noise is greatly enhanced in assortative (positively correlated) neural networks, especially if it is the hub neurons that store the information.

  3. High-performance neural networks. [Neural computers

    SciTech Connect

    Dress, W.B.

    1987-06-01

    The new Forth hardware architectures offer an intermediate solution to high-performance neural networks while the theory and programming details of neural networks for synthetic intelligence are developed. This approach has been used successfully to determine the parameters and run the resulting network for a synthetic insect consisting of a 200-node ''brain'' with 1760 interconnections. Both the insect's environment and its sensor input have thus far been simulated. However, the frequency-coded nature of the Browning network allows easy replacement of the simulated sensors by real-world counterparts.

  4. Enhancing neural-network performance via assortativity.

    PubMed

    de Franciscis, Sebastiano; Johnson, Samuel; Torres, Joaquín J

    2011-03-01

    The performance of attractor neural networks has been shown to depend crucially on the heterogeneity of the underlying topology. We take this analysis a step further by examining the effect of degree-degree correlations--assortativity--on neural-network behavior. We make use of a method recently put forward for studying correlated networks and dynamics thereon, both analytically and computationally, which is independent of how the topology may have evolved. We show how the robustness to noise is greatly enhanced in assortative (positively correlated) neural networks, especially if it is the hub neurons that store the information.

  5. Belief network algorithms: A study of performance

    SciTech Connect

    Jitnah, N.

    1996-12-31

    This abstract gives an overview of the work. We present a survey of Belief Network algorithms and propose a domain characterization system to be used as a basis for algorithm comparison and for predicting algorithm performance.

  6. Measurements by a Vector Network Analyzer at 325 to 508 GHz

    NASA Technical Reports Server (NTRS)

    Fung, King Man; Samoska, Lorene; Chattopadhyay, Goutam; Gaier, Todd; Kangaslahti, Pekka; Pukala, David; Lau, Yuenie; Oleson, Charles; Denning, Anthony

    2008-01-01

    Recent experiments were performed in which return loss and insertion loss of waveguide test assemblies in the frequency range from 325 to 508 GHz were measured by use of a swept-frequency two-port vector network analyzer (VNA) test set. The experiments were part of a continuing effort to develop means of characterizing passive and active electronic components and systems operating at ever increasing frequencies. The waveguide test assemblies comprised WR-2.2 end sections collinear with WR-3.3 middle sections. The test set, assembled from commercially available components, included a 50-GHz VNA scattering- parameter test set and external signal synthesizers, augmented with recently developed frequency extenders, and further augmented with attenuators and amplifiers as needed to adjust radiofrequency and intermediate-frequency power levels between the aforementioned components. The tests included line-reflect-line calibration procedures, using WR-2.2 waveguide shims as the "line" standards and waveguide flange short circuits as the "reflect" standards. Calibrated dynamic ranges somewhat greater than about 20 dB for return loss and 35 dB for insertion loss were achieved. The measurement data of the test assemblies were found to substantially agree with results of computational simulations.

  7. Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis.

    PubMed

    Gopal, Shruti; Miller, Robyn L; Michael, Andrew; Adali, Tulay; Cetin, Mustafa; Rachakonda, Srinivas; Bustillo, Juan R; Cahill, Nathan; Baum, Stefi A; Calhoun, Vince D

    2016-01-01

    Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in schizophrenia, a disease known for both neurodevelopmental and widespread anatomic changes. Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw upon a relatively new approach called independent vector analysis (IVA) to assess this variability in resting fMRI networks. IVA is a blind-source separation algorithm, which segregates fMRI data into temporally coherent but spatially independent networks and has been shown to be especially good at capturing spatial variability among subjects in the extracted networks. We introduce several new ways to quantify differences in variability of IVA-derived networks between schizophrenia patients (SZs = 82) and healthy controls (HCs = 89). Voxelwise amplitude analyses showed significant group differences in the spatial maps of auditory cortex, the basal ganglia, the sensorimotor network, and visual cortex. Tests for differences (HC-SZ) in the spatial variability maps suggest, that at rest, SZs exhibit more activity within externally focused sensory and integrative network and less activity in the default mode network thought to be related to internal reflection. Additionally, tests for difference of variance between groups further emphasize that SZs exhibit greater network variability. These results, consistent with our prediction of increased spatial variability within SZs, enhance our understanding of the disease and suggest that it is not just the amplitude of connectivity that is different in schizophrenia, but also the consistency in spatial connectivity patterns across subjects.

  8. Comparing error minimized extreme learning machines and support vector sequential feed-forward neural networks.

    PubMed

    Romero, Enrique; Alquézar, René

    2012-01-01

    Recently, error minimized extreme learning machines (EM-ELMs) have been proposed as a simple and efficient approach to build single-hidden-layer feed-forward networks (SLFNs) sequentially. They add random hidden nodes one by one (or group by group) and update the output weights incrementally to minimize the sum-of-squares error in the training set. Other very similar methods that also construct SLFNs sequentially had been reported earlier with the main difference that their hidden-layer weights are a subset of the data instead of being random. These approaches are referred to as support vector sequential feed-forward neural networks (SV-SFNNs), and they are a particular case of the sequential approximation with optimal coefficients and interacting frequencies (SAOCIF) method. In this paper, it is firstly shown that EM-ELMs can also be cast as a particular case of SAOCIF. In particular, EM-ELMs can easily be extended to test some number of random candidates at each step and select the best of them, as SAOCIF does. Moreover, it is demonstrated that the cost of the computation of the optimal output-layer weights in the originally proposed EM-ELMs can be improved if it is replaced by the one included in SAOCIF. Secondly, we present the results of an experimental study on 10 benchmark classification and 10 benchmark regression data sets, comparing EM-ELMs and SV-SFNNs, that was carried out under the same conditions for the two models. Although both models have the same (efficient) computational cost, a statistically significant improvement in generalization performance of SV-SFNNs vs. EM-ELMs was found in 12 out of the 20 benchmark problems.

  9. Performance analysis of a VSAT network

    NASA Astrophysics Data System (ADS)

    Karam, Fouad G.; Miller, Neville; Karam, Antoine

    With the growing need for efficient satellite networking facilities, the very small aperture terminal (VSAT) technology emerges as the leading edge of satellite communications. Achieving the required performance of a VSAT network is dictated by the multiple access technique utilized. Determining the inbound access method best suited for a particular application involves trade-offs between response time and space segment utilization. In this paper, the slotted Aloha and dedicated stream access techniques are compared. It is shown that network performance is dependent on the traffic offered from remote earth stations as well as the sensitivity of customer's applications to satellite delay.

  10. Evaluation of Raman spectra of human brain tumor tissue using the learning vector quantization neural network

    NASA Astrophysics Data System (ADS)

    Liu, Tuo; Chen, Changshui; Shi, Xingzhe; Liu, Chengyong

    2016-05-01

    The Raman spectra of tissue of 20 brain tumor patients was recorded using a confocal microlaser Raman spectroscope with 785 nm excitation in vitro. A total of 133 spectra were investigated. Spectra peaks from normal white matter tissue and tumor tissue were analyzed. Algorithms, such as principal component analysis, linear discriminant analysis, and the support vector machine, are commonly used to analyze spectral data. However, in this study, we employed the learning vector quantization (LVQ) neural network, which is typically used for pattern recognition. By applying the proposed method, a normal diagnosis accuracy of 85.7% and a glioma diagnosis accuracy of 89.5% were achieved. The LVQ neural network is a recent approach to excavating Raman spectra information. Moreover, it is fast and convenient, does not require the spectra peak counterpart, and achieves a relatively high accuracy. It can be used in brain tumor prognostics and in helping to optimize the cutting margins of gliomas.

  11. Speech recognition method based on genetic vector quantization and BP neural network

    NASA Astrophysics Data System (ADS)

    Gao, Li'ai; Li, Lihua; Zhou, Jian; Zhao, Qiuxia

    2009-07-01

    Vector Quantization is one of popular codebook design methods for speech recognition at present. In the process of codebook design, traditional LBG algorithm owns the advantage of fast convergence, but it is easy to get the local optimal result and be influenced by initial codebook. According to the understanding that Genetic Algorithm has the capability of getting the global optimal result, this paper proposes a hybrid clustering method GA-L based on Genetic Algorithm and LBG algorithm to improve the codebook.. Then using genetic neural networks for speech recognition. consequently search a global optimization codebook of the training vector space. The experiments show that neural network identification method based on genetic algorithm can extricate from its local maximum value and the initial restrictions, it can show superior to the standard genetic algorithm and BP neural network algorithm from various sources, and the genetic BP neural networks has a higher recognition rate and the unique application advantages than the general BP neural network in the same GA-VQ codebook, it can achieve a win-win situation in the time and efficiency.

  12. Performance benchmarking of core optical networking paradigms.

    PubMed

    Drakos, Andreas; Orphanoudakis, Theofanis G; Stavdas, Alexandros

    2012-07-30

    The sustainability of Future Internet critically depends on networking paradigms able to provide optimum and balanced performance over an extended set of efficiency and Quality of Service (QoS) metrics. In this work we benchmark the most established networking modes through appropriate performance metrics for three network topologies. The results demonstrate that the static reservation of WDM channels, as used in IP/WDM schemes, is severely limiting scalability, since it cannot efficiently adapt to the dynamic traffic fluctuations that are frequently observed in today's networks. Optical Burst Switching (OBS) schemes do provide dynamic resource reservation but their performance is compromised due to high burst loss. It is shown that the CANON (Clustered Architecture for Nodes in an Optical Network) architecture exploiting statistical multiplexing over a large scale core optical network and efficient grooming at appropriate granularity levels could be a viable alternative to existing static as well as dynamic wavelength reservation schemes. Through extensive simulation results we quantify performance gains and we show that CANON demonstrates the highest efficiency achieving both targets for statistical multiplexing gains and QoS guarantees.

  13. Diversity Performance Analysis on Multiple HAP Networks

    PubMed Central

    Dong, Feihong; Li, Min; Gong, Xiangwu; Li, Hongjun; Gao, Fengyue

    2015-01-01

    One of the main design challenges in wireless sensor networks (WSNs) is achieving a high-data-rate transmission for individual sensor devices. The high altitude platform (HAP) is an important communication relay platform for WSNs and next-generation wireless networks. Multiple-input multiple-output (MIMO) techniques provide the diversity and multiplexing gain, which can improve the network performance effectively. In this paper, a virtual MIMO (V-MIMO) model is proposed by networking multiple HAPs with the concept of multiple assets in view (MAV). In a shadowed Rician fading channel, the diversity performance is investigated. The probability density function (PDF) and cumulative distribution function (CDF) of the received signal-to-noise ratio (SNR) are derived. In addition, the average symbol error rate (ASER) with BPSK and QPSK is given for the V-MIMO model. The system capacity is studied for both perfect channel state information (CSI) and unknown CSI individually. The ergodic capacity with various SNR and Rician factors for different network configurations is also analyzed. The simulation results validate the effectiveness of the performance analysis. It is shown that the performance of the HAPs network in WSNs can be significantly improved by utilizing the MAV to achieve overlapping coverage, with the help of the V-MIMO techniques. PMID:26134102

  14. Performance analysis of distributed symmetric sparse matrix vector multiplication algorithm for multi-core architectures

    DOE PAGES

    Oryspayev, Dossay; Aktulga, Hasan Metin; Sosonkina, Masha; ...

    2015-07-14

    In this article, sparse matrix vector multiply (SpMVM) is an important kernel that frequently arises in high performance computing applications. Due to its low arithmetic intensity, several approaches have been proposed in literature to improve its scalability and efficiency in large scale computations. In this paper, our target systems are high end multi-core architectures and we use messaging passing interface + open multiprocessing hybrid programming model for parallelism. We analyze the performance of recently proposed implementation of the distributed symmetric SpMVM, originally developed for large sparse symmetric matrices arising in ab initio nuclear structure calculations. We also study important featuresmore » of this implementation and compare with previously reported implementations that do not exploit underlying symmetry. Our SpMVM implementations leverage the hybrid paradigm to efficiently overlap expensive communications with computations. Our main comparison criterion is the "CPU core hours" metric, which is the main measure of resource usage on supercomputers. We analyze the effects of topology-aware mapping heuristic using simplified network load model. Furthermore, we have tested the different SpMVM implementations on two large clusters with 3D Torus and Dragonfly topology. Our results show that the distributed SpMVM implementation that exploits matrix symmetry and hides communication yields the best value for the "CPU core hours" metric and significantly reduces data movement overheads.« less

  15. Performance analysis of distributed symmetric sparse matrix vector multiplication algorithm for multi-core architectures

    SciTech Connect

    Oryspayev, Dossay; Aktulga, Hasan Metin; Sosonkina, Masha; Maris, Pieter; Vary, James P.

    2015-07-14

    In this article, sparse matrix vector multiply (SpMVM) is an important kernel that frequently arises in high performance computing applications. Due to its low arithmetic intensity, several approaches have been proposed in literature to improve its scalability and efficiency in large scale computations. In this paper, our target systems are high end multi-core architectures and we use messaging passing interface + open multiprocessing hybrid programming model for parallelism. We analyze the performance of recently proposed implementation of the distributed symmetric SpMVM, originally developed for large sparse symmetric matrices arising in ab initio nuclear structure calculations. We also study important features of this implementation and compare with previously reported implementations that do not exploit underlying symmetry. Our SpMVM implementations leverage the hybrid paradigm to efficiently overlap expensive communications with computations. Our main comparison criterion is the "CPU core hours" metric, which is the main measure of resource usage on supercomputers. We analyze the effects of topology-aware mapping heuristic using simplified network load model. Furthermore, we have tested the different SpMVM implementations on two large clusters with 3D Torus and Dragonfly topology. Our results show that the distributed SpMVM implementation that exploits matrix symmetry and hides communication yields the best value for the "CPU core hours" metric and significantly reduces data movement overheads.

  16. Predictable nonwandering localization of covariant Lyapunov vectors and cluster synchronization in scale-free networks of chaotic maps.

    PubMed

    Kuptsov, Pavel V; Kuptsova, Anna V

    2014-09-01

    Covariant Lyapunov vectors for scale-free networks of Hénon maps are highly localized. We revealed two mechanisms of the localization related to full and phase cluster synchronization of network nodes. In both cases the localization nodes remain unaltered in the course of the dynamics, i.e., the localization is nonwandering. Moreover, this is predictable: The localization nodes are found to have specific dynamical and topological properties and they can be found without computing of the covariant vectors. This is an example of explicit relations between the system topology, its phase-space dynamics, and the associated tangent-space dynamics of covariant Lyapunov vectors.

  17. Static performance investigation of a skewed-throat multiaxis thrust-vectoring nozzle concept

    NASA Technical Reports Server (NTRS)

    Wing, David J.

    1994-01-01

    The static performance of a jet exhaust nozzle which achieves multiaxis thrust vectoring by physically skewing the geometric throat has been characterized in the static test facility of the 16-Foot Transonic Tunnel at NASA Langley Research Center. The nozzle has an asymmetric internal geometry defined by four surfaces: a convergent-divergent upper surface with its ridge perpendicular to the nozzle centerline, a convergent-divergent lower surface with its ridge skewed relative to the nozzle centerline, an outwardly deflected sidewall, and a straight sidewall. The primary goal of the concept is to provide efficient yaw thrust vectoring by forcing the sonic plane (nozzle throat) to form at a yaw angle defined by the skewed ridge of the lower surface contour. A secondary goal is to provide multiaxis thrust vectoring by combining the skewed-throat yaw-vectoring concept with upper and lower pitch flap deflections. The geometric parameters varied in this investigation included lower surface ridge skew angle, nozzle expansion ratio (divergence angle), aspect ratio, pitch flap deflection angle, and sidewall deflection angle. Nozzle pressure ratio was varied from 2 to a high of 11.5 for some configurations. The results of the investigation indicate that efficient, substantial multiaxis thrust vectoring was achieved by the skewed-throat nozzle concept. However, certain control surface deflections destabilized the internal flow field, which resulted in substantial shifts in the position and orientation of the sonic plane and had an adverse effect on thrust-vectoring and weight flow characteristics. By increasing the expansion ratio, the location of the sonic plane was stabilized. The asymmetric design resulted in interdependent pitch and yaw thrust vectoring as well as nonzero thrust-vector angles with undeflected control surfaces. By skewing the ridges of both the upper and lower surface contours, the interdependency between pitch and yaw thrust vectoring may be eliminated

  18. Initial Flight Test Evaluation of the F-15 ACTIVE Axisymmetric Vectoring Nozzle Performance

    NASA Technical Reports Server (NTRS)

    Orme, John S.; Hathaway, Ross; Ferguson, Michael D.

    1998-01-01

    A full envelope database of a thrust-vectoring axisymmetric nozzle performance for the Pratt & Whitney Pitch/Yaw Balance Beam Nozzle (P/YBBN) is being developed using the F-15 Advanced Control Technology for Integrated Vehicles (ACTIVE) aircraft. At this time, flight research has been completed for steady-state pitch vector angles up to 20' at an altitude of 30,000 ft from low power settings to maximum afterburner power. The nozzle performance database includes vector forces, internal nozzle pressures, and temperatures all of which can be used for regression analysis modeling. The database was used to substantiate a set of nozzle performance data from wind tunnel testing and computational fluid dynamic analyses. Findings from initial flight research at Mach 0.9 and 1.2 are presented in this paper. The results show that vector efficiency is strongly influenced by power setting. A significant discrepancy in nozzle performance has been discovered between predicted and measured results during vectoring.

  19. On-wafer vector network analyzer measurements in the 220-325 Ghz frequency band

    NASA Technical Reports Server (NTRS)

    Fung, King Man Andy; Dawson, D.; Samoska, L.; Lee, K.; Oleson, C.; Boll, G.

    2006-01-01

    We report on a full two-port on-wafer vector network analyzer test set for the 220-325 GHz (WR3) frequency band. The test set utilizes Oleson Microwave Labs frequency extenders with the Agilent 8510C network analyzer. Two port on-wafer measurements are made with GGB Industries coplanar waveguide (CPW) probes. With this test set we have measured the WR3 band S-parameters of amplifiers on-wafer, and the characteristics of the CPW wafer probes. Results for a three stage InP HEMT amplifier show 10 dB gain at 235 GHz [1], and that of a single stage amplifier, 2.9 dB gain at 231 GHz. The approximate upper limit of loss per CPW probe range from 3.0 to 4.8 dB across the WR3 frequency band.

  20. Understanding transmissibility patterns of Chagas disease through complex vector-host networks.

    PubMed

    Rengifo-Correa, Laura; Stephens, Christopher R; Morrone, Juan J; Téllez-Rendón, Juan Luis; González-Salazar, Constantino

    2017-01-12

    Chagas disease is one of the most important vector-borne zoonotic diseases in Latin America. Control strategies could be improved if transmissibility patterns of its aetiologic agent, Trypanosoma cruzi, were better understood. To understand transmissibility patterns of Chagas disease in Mexico, we inferred potential vectors and hosts of T. cruzi from geographic distributions of nine species of Triatominae and 396 wild mammal species, respectively. The most probable vectors and hosts of T. cruzi were represented in a Complex Inference Network, from which we formulated a predictive model and several associated hypotheses about the ecological epidemiology of Chagas disease. We compiled a list of confirmed mammal hosts to test our hypotheses. Our tests allowed us to predict the most important potential hosts of T. cruzi and to validate the model showing that the confirmed hosts were those predicted to be the most important hosts. We were also able to predict differences in the transmissibility of T. cruzi among triatomine species from spatial data. We hope our findings help drive efforts for future experimental studies.

  1. Networked Chemoreceptors Benefit Bacterial Chemotaxis Performance

    PubMed Central

    Frank, Vered; Piñas, Germán E.; Cohen, Harel; Parkinson, John S.

    2016-01-01

    ABSTRACT Motile bacteria use large receptor arrays to detect and follow chemical gradients in their environment. Extended receptor arrays, composed of networked signaling complexes, promote cooperative stimulus control of their associated signaling kinases. Here, we used structural lesions at the communication interface between core complexes to create an Escherichia coli strain with functional but dispersed signaling complexes. This strain allowed us to directly study how networking of signaling complexes affects chemotactic signaling and gradient-tracking performance. We demonstrate that networking of receptor complexes provides bacterial cells with about 10-fold-heightened detection sensitivity to attractants while maintaining a wide dynamic range over which receptor adaptational modifications can tune response sensitivity. These advantages proved especially critical for chemotaxis toward an attractant source under conditions in which bacteria are unable to alter the attractant gradient. PMID:27999161

  2. Analysis of a general SIS model with infective vectors on the complex networks

    NASA Astrophysics Data System (ADS)

    Juang, Jonq; Liang, Yu-Hao

    2015-11-01

    A general SIS model with infective vectors on complex networks is studied in this paper. In particular, the model considers the linear combination of three possible routes of disease propagation between infected and susceptible individuals as well as two possible transmission types which describe how the susceptible vectors attack the infected individuals. A new technique based on the basic reproduction matrix is introduced to obtain the following results. First, necessary and sufficient conditions are obtained for the global stability of the model through a unified approach. As a result, we are able to produce the exact basic reproduction number and the precise epidemic thresholds with respect to three spreading strengths, the curing strength or the immunization strength all at once. Second, the monotonicity of the basic reproduction number and the above mentioned epidemic thresholds with respect to all other parameters can be rigorously characterized. Finally, we are able to compare the effectiveness of various immunization strategies under the assumption that the number of persons getting vaccinated is the same for all strategies. In particular, we prove that in the scale-free networks, both targeted and acquaintance immunizations are more effective than uniform and active immunizations and that active immunization is the least effective strategy among those four. We are also able to determine how the vaccine should be used at minimum to control the outbreak of the disease.

  3. Calibration-measurement unit for the automation of vector network analyzer measurements

    NASA Astrophysics Data System (ADS)

    Rolfes, I.; Will, B.; Schiek, B.

    2008-05-01

    With the availability of multi-port vector network analyzers, the need for automated, calibrated measurement facilities increases. In this contribution, a calibration-measurement unit is presented which realizes a repeatable automated calibration of the measurement setup as well as a user-friendly measurement of the device under test (DUT). In difference to commercially available calibration units, which are connected to the ports of the vector network analyzer preceding a measurement and which are then removed so that the DUT can be connected, the presented calibration-measurement unit is permanently connected to the ports of the VNA for the calibration as well as for the measurement of the DUT. This helps to simplify the calibrated measurement of complex scattering parameters. Moreover, a full integration of the calibration unit into the analyzer setup becomes possible. The calibration-measurement unit is based on a multiport switch setup of e.g. electromechanical relays. Under the assumption of symmetry of a switch, on the one hand the unit realizes the connection of calibration standards like one-port reflection standards and two-port through connections between different ports and on the other hand it enables the connection of the DUT. The calibration-measurement unit is applicable for two-port VNAs as well as for multiport VNAs. For the calibration of the unit, methods with completely known calibration standards like SOLT (short, open, load, through) as well as self-calibration procedures like TMR or TLR can be applied.

  4. Static performance of a cruciform nozzle with multiaxis thrust-vectoring and reverse-thrust capabilities

    NASA Technical Reports Server (NTRS)

    Wing, David J.; Asbury, Scott C.

    1992-01-01

    A multiaxis thrust vectoring nozzle designed to have equal flow turning capability in pitch and yaw was conceived and experimentally tested for internal, static performance. The cruciform-shaped convergent-divergent nozzle turned the flow for thrust vectoring by deflecting the divergent surfaces of the nozzle, called flaps. Methods for eliminating physical interference between pitch and yaw flaps at the larger multiaxis deflection angles was studied. These methods included restricting the pitch flaps from the path of the yaw flaps and shifting the flow path at the throat off the nozzle centerline to permit larger pitch-flap deflections without interfering with the operation of the yaw flaps. Two flap widths were tested at both dry and afterburning settings. Vertical and reverse thrust configurations at dry power were also tested. Comparison with two dimensional convergent-divergent nozzles showed lower but still competitive thrust performance and thrust vectoring capability.

  5. Performance of TCP variants over LTE network

    NASA Astrophysics Data System (ADS)

    Nor, Shahrudin Awang; Maulana, Ade Novia

    2016-08-01

    One of the implementation of a wireless network is based on mobile broadband technology Long Term Evolution (LTE). LTE offers a variety of advantages, especially in terms of access speed, capacity, architectural simplicity and ease of implementation, as well as the breadth of choice of the type of user equipment (UE) that can establish the access. The majority of the Internet connections in the world happen using the TCP (Transmission Control Protocol) due to the TCP's reliability in transmitting packets in the network. TCP reliability lies in the ability to control the congestion. TCP was originally designed for wired media, but LTE connected through a wireless medium that is not stable in comparison to wired media. A wide variety of TCP has been made to produce a better performance than its predecessor. In this study, we simulate the performance provided by the TCP NewReno and TCP Vegas based on simulation using network simulator version 2 (ns2). The TCP performance is analyzed in terms of throughput, packet loss and end-to-end delay. In comparing the performance of TCP NewReno and TCP Vegas, the simulation result shows that the throughput of TCP NewReno is slightly higher than TCP Vegas, while TCP Vegas gives significantly better end-to-end delay and packet loss. The analysis of throughput, packet loss and end-to-end delay are made to evaluate the simulation.

  6. Neurodynamics of learning and network performance

    NASA Astrophysics Data System (ADS)

    Wilson, Charles L.; Blue, James L.; Omidvar, Omid M.

    1997-07-01

    A simple dynamic model of a neural network is presented. Using the dynamic model of a neural network, we improve the performance of a three-layer multilayer perceptron (MLP). The dynamic model of a MLP is used to make fundamental changes in the network optimization strategy. These changes are: neuron activation functions are used, which reduces the probability of singular Jacobians; successive regularization is used to constrain the volume of the weight space being minimized; Boltzmann pruning is used to constrain the dimension of the weight space; and prior class probabilities are used to normalize all error calculations, so that statistically significant samples of rare but important classes can be included without distortion of the error surface. All four of these changes are made in the inner loop of a conjugate gradient optimization iteration and are intended to simplify the training dynamics of the optimization. On handprinted digits and fingerprint classification problems, these modifications improve error-reject performance by factors between 2 and 4 and reduce network size by 40 to 60%.

  7. 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.

  8. The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: Overview and Performance

    NASA Astrophysics Data System (ADS)

    Hoeksema, J. Todd; Liu, Yang; Hayashi, Keiji; Sun, Xudong; Schou, Jesper; Couvidat, Sebastien; Norton, Aimee; Bobra, Monica; Centeno, Rebecca; Leka, K. D.; Barnes, Graham; Turmon, Michael

    2014-09-01

    The Helioseismic and Magnetic Imager (HMI) began near-continuous full-disk solar measurements on 1 May 2010 from the Solar Dynamics Observatory (SDO). An automated processing pipeline keeps pace with observations to produce observable quantities, including the photospheric vector magnetic field, from sequences of filtergrams. The basic vector-field frame list cadence is 135 seconds, but to reduce noise the filtergrams are combined to derive data products every 720 seconds. The primary 720 s observables were released in mid-2010, including Stokes polarization parameters measured at six wavelengths, as well as intensity, Doppler velocity, and the line-of-sight magnetic field. More advanced products, including the full vector magnetic field, are now available. Automatically identified HMI Active Region Patches (HARPs) track the location and shape of magnetic regions throughout their lifetime. The vector field is computed using the Very Fast Inversion of the Stokes Vector (VFISV) code optimized for the HMI pipeline; the remaining 180∘ azimuth ambiguity is resolved with the Minimum Energy (ME0) code. The Milne-Eddington inversion is performed on all full-disk HMI observations. The disambiguation, until recently run only on HARP regions, is now implemented for the full disk. Vector and scalar quantities in the patches are used to derive active region indices potentially useful for forecasting; the data maps and indices are collected in the SHARP data series, hmi.sharp_720s. Definitive SHARP processing is completed only after the region rotates off the visible disk; quick-look products are produced in near real time. Patches are provided in both CCD and heliographic coordinates. HMI provides continuous coverage of the vector field, but has modest spatial, spectral, and temporal resolution. Coupled with limitations of the analysis and interpretation techniques, effects of the orbital velocity, and instrument performance, the resulting measurements have a certain dynamic

  9. Characterization of interdigitated electrode structures for water contaminant detection using a hybrid voltage divider and a vector network analyzer.

    PubMed

    Rodríguez-Delgado, José Manuel; Rodríguez-Delgado, Melissa Marlene; Mendoza-Buenrostro, Christian; Dieck-Assad, Graciano; Omar Martínez-Chapa, Sergio

    2012-01-01

    Interdigitated capacitive electrode structures have been used to monitor or actuate over organic and electrochemical media in efforts to characterize biochemical properties. This article describes a method to perform a pre-characterization of interdigitated electrode structures using two methods: a hybrid voltage divider (HVD) and a vector network analyzer (VNA). Both methodologies develop some tests under two different conditions: free air and bi-distilled water media. Also, the HVD methodology is used for other two conditions: phosphate buffer with laccase (polyphenoloxidase; EC 1.10.3.2) and contaminated media composed by a mix of phosphate buffer and 3-ethylbenzothiazoline-6-sulfonic acid (ABTS). The purpose of this study is to develop and validate a characterization methodology using both, a hybrid voltage divider and VNA T-# network impedance models of the interdigitated capacitive electrode structure that will provide a shunt RC network of particular interest in detecting the amount of contamination existing in the water solution for the media conditions. This methodology should provide us with the best possible sensitivity in monitoring water contaminant media characteristics. The results show that both methods, the hybrid voltage divider and the VNA methodology, are feasible in determining impedance modeling parameters. These parameters can be used to develop electric interrogation procedures and devices such as dielectric characteristics to identify contaminant substances in water solutions.

  10. Static internal performance of single expansion-ramp nozzles with thrust vectoring and reversing

    NASA Technical Reports Server (NTRS)

    Re, R. J.; Berrier, B. L.

    1982-01-01

    The effects of geometric design parameters on the internal performance of nonaxisymmetric single expansion-ramp nozzles were investigated at nozzle pressure ratios up to approximately 10. Forward-flight (cruise), vectored-thrust, and reversed-thrust nozzle operating modes were investigated.

  11. Static Thrust and Vectoring Performance of a Spherical Convergent Flap Nozzle with a Nonrectangular Divergent Duct

    NASA Technical Reports Server (NTRS)

    Wing, David J.

    1998-01-01

    The static internal performance of a multiaxis-thrust-vectoring, spherical convergent flap (SCF) nozzle with a non-rectangular divergent duct was obtained in the model preparation area of the Langley 16-Foot Transonic Tunnel. Duct cross sections of hexagonal and bowtie shapes were tested. Additional geometric parameters included throat area (power setting), pitch flap deflection angle, and yaw gimbal angle. Nozzle pressure ratio was varied from 2 to 12 for dry power configurations and from 2 to 6 for afterburning power configurations. Approximately a 1-percent loss in thrust efficiency from SCF nozzles with a rectangular divergent duct was incurred as a result of internal oblique shocks in the flow field. The internal oblique shocks were the result of cross flow generated by the vee-shaped geometric throat. The hexagonal and bowtie nozzles had mirror-imaged flow fields and therefore similar thrust performance. Thrust vectoring was not hampered by the three-dimensional internal geometry of the nozzles. Flow visualization indicates pitch thrust-vector angles larger than 10' may be achievable with minimal adverse effect on or a possible gain in resultant thrust efficiency as compared with the performance at a pitch thrust-vector angle of 10 deg.

  12. Modeling and Performance Simulation of the Mass Storage Network Environment

    NASA Technical Reports Server (NTRS)

    Kim, Chan M.; Sang, Janche

    2000-01-01

    This paper describes the application of modeling and simulation in evaluating and predicting the performance of the mass storage network environment. Network traffic is generated to mimic the realistic pattern of file transfer, electronic mail, and web browsing. The behavior and performance of the mass storage network and a typical client-server Local Area Network (LAN) are investigated by modeling and simulation. Performance characteristics in throughput and delay demonstrate the important role of modeling and simulation in network engineering and capacity planning.

  13. Scientific Application Performance on Leading Scalar and VectorSupercomputing Platforms

    SciTech Connect

    Oliker, Leonid; Canning, Andrew; Carter, Jonathan; Shalf, John; Ethier, Stephane

    2007-01-01

    The last decade has witnessed a rapid proliferation of superscalar cache-based microprocessors to build high-end computing (HEC) platforms, primarily because of their generality, scalability, and cost effectiveness. However, the growing gap between sustained and peak performance for full-scale scientific applications on conventional supercomputers has become a major concern in high performance computing, requiring significantly larger systems and application scalability than implied by peak performance in order to achieve desired performance. The latest generation of custom-built parallel vector systems have the potential to address this issue for numerical algorithms with sufficient regularity in their computational structure. In this work we explore applications drawn from four areas: magnetic fusion (GTC), plasma physics (LBMHD3D), astrophysics (Cactus), and material science (PARATEC). We compare performance of the vector-based Cray X1, X1E, Earth Simulator, NEC SX-8, with performance of three leading commodity-based superscalar platforms utilizing the IBM Power3, Intel Itanium2, and AMD Opteron processors. Our work makes several significant contributions: a new data-decomposition scheme for GTC that (for the first time) enables a breakthrough of the Teraflop barrier; the introduction of a new three-dimensional Lattice Boltzmann magneto-hydrodynamic implementation used to study the onset evolution of plasma turbulence that achieves over 26Tflop/s on 4800 ES processors; the highest per processor performance (by far) achieved by the full-production version of the Cactus ADM-BSSN; and the largest PARATEC cell size atomistic simulation to date. Overall, results show that the vector architectures attain unprecedented aggregate performance across our application suite, demonstrating the tremendous potential of modern parallel vector systems.

  14. Cluster Expansion Method for Evolving Weighted Networks Having Vector-Like Nodes

    NASA Astrophysics Data System (ADS)

    Ausloos, M.; Gligor, M.

    2008-09-01

    The cluster variation method known in statistical mechanics and condensed matter is revived for weighted bipartite networks. The decomposition (or expansion) of a Hamiltonian through a finite number of components, whence serving to define variable clusters, is recalled. As an illustration the network built from data representing correlations between (4) macroeconomic features, i.e. the so-called vector components, of 15 EU countries, as (function) nodes, is discussed. We show that statistical physics principles, like the maximum entropy criterion points to clusters, here in a (4) variable phase space: Gross Domestic Product, Final Consumption Expenditure, Gross Capital Formation and Net Exports. It is observed that the maximum entropy corresponds to a cluster which does not explicitly include the Gross Domestic Product but only the other (3) "axes", i.e. consumption, investment and trade components. On the other hand, the minimal entropy clustering scheme is obtained from a coupling necessarily including Gross Domestic Product and Final Consumption Expenditure. The results confirm intuitive economic theory and practice expectations at least as regards geographical connexions. The technique can of course be applied to many other cases in the physics of socio-economy networks.

  15. Artificial neural network simulation of battery performance

    SciTech Connect

    O`Gorman, C.C.; Ingersoll, D.; Jungst, R.G.; Paez, T.L.

    1998-12-31

    Although they appear deceptively simple, batteries embody a complex set of interacting physical and chemical processes. While the discrete engineering characteristics of a battery such as the physical dimensions of the individual components, are relatively straightforward to define explicitly, their myriad chemical and physical processes, including interactions, are much more difficult to accurately represent. Within this category are the diffusive and solubility characteristics of individual species, reaction kinetics and mechanisms of primary chemical species as well as intermediates, and growth and morphology characteristics of reaction products as influenced by environmental and operational use profiles. For this reason, development of analytical models that can consistently predict the performance of a battery has only been partially successful, even though significant resources have been applied to this problem. As an alternative approach, the authors have begun development of a non-phenomenological model for battery systems based on artificial neural networks. Both recurrent and non-recurrent forms of these networks have been successfully used to develop accurate representations of battery behavior. The connectionist normalized linear spline (CMLS) network has been implemented with a self-organizing layer to model a battery system with the generalized radial basis function net. Concurrently, efforts are under way to use the feedforward back propagation network to map the {open_quotes}state{close_quotes} of a battery system. Because of the complexity of battery systems, accurate representation of the input and output parameters has proven to be very important. This paper describes these initial feasibility studies as well as the current models and makes comparisons between predicted and actual performance.

  16. Deep learning of support vector machines with class probability output networks.

    PubMed

    Kim, Sangwook; Yu, Zhibin; Kil, Rhee Man; Lee, Minho

    2015-04-01

    Deep learning methods endeavor to learn features automatically at multiple levels and allow systems to learn complex functions mapping from the input space to the output space for the given data. The ability to learn powerful features automatically is increasingly important as the volume of data and range of applications of machine learning methods continues to grow. This paper proposes a new deep architecture that uses support vector machines (SVMs) with class probability output networks (CPONs) to provide better generalization power for pattern classification problems. As a result, deep features are extracted without additional feature engineering steps, using multiple layers of the SVM classifiers with CPONs. The proposed structure closely approaches the ideal Bayes classifier as the number of layers increases. Using a simulation of classification problems, the effectiveness of the proposed method is demonstrated.

  17. Advancements and performance of iterative methods in industrial applications codes on CRAY parallel/vector supercomputers

    SciTech Connect

    Poole, G.; Heroux, M.

    1994-12-31

    This paper will focus on recent work in two widely used industrial applications codes with iterative methods. The ANSYS program, a general purpose finite element code widely used in structural analysis applications, has now added an iterative solver option. Some results are given from real applications comparing performance with the tradition parallel/vector frontal solver used in ANSYS. Discussion of the applicability of iterative solvers as a general purpose solver will include the topics of robustness, as well as memory requirements and CPU performance. The FIDAP program is a widely used CFD code which uses iterative solvers routinely. A brief description of preconditioners used and some performance enhancements for CRAY parallel/vector systems is given. The solution of large-scale applications in structures and CFD includes examples from industry problems solved on CRAY systems.

  18. Functional Forms of Optimum Spoofing Attacks for Vector Parameter Estimation in Quantized Sensor Networks

    NASA Astrophysics Data System (ADS)

    Zhang, Jiangfan; Blum, Rick S.; Kaplan, Lance M.; Lu, Xuanxuan

    2017-02-01

    Estimation of an unknown deterministic vector from quantized sensor data is considered in the presence of spoofing attacks which alter the data presented to several sensors. Contrary to previous work, a generalized attack model is employed which manipulates the data using transformations with arbitrary functional forms determined by some attack parameters whose values are unknown to the attacked system. For the first time, necessary and sufficient conditions are provided under which the transformations provide a guaranteed attack performance in terms of Cramer-Rao Bound (CRB) regardless of the processing the estimation system employs, thus defining a highly desirable attack. Interestingly, these conditions imply that, for any such attack when the attacked sensors can be perfectly identified by the estimation system, either the Fisher Information Matrix (FIM) for jointly estimating the desired and attack parameters is singular or that the attacked system is unable to improve the CRB for the desired vector parameter through this joint estimation even though the joint FIM is nonsingular. It is shown that it is always possible to construct such a highly desirable attack by properly employing a sufficiently large dimension attack vector parameter relative to the number of quantization levels employed, which was not observed previously. To illustrate the theory in a concrete way, we also provide some numerical results which corroborate that under the highly desirable attack, attacked data is not useful in reducing the CRB.

  19. Dynamic changes of spatial functional network connectivity in healthy individuals and schizophrenia patients using independent vector analysis.

    PubMed

    Ma, Sai; Calhoun, Vince D; Phlypo, Ronald; Adalı, Tülay

    2014-04-15

    Recent work on both task-induced and resting-state functional magnetic resonance imaging (fMRI) data suggests that functional connectivity may fluctuate, rather than being stationary during an entire scan. Most dynamic studies are based on second-order statistics between fMRI time series or time courses derived from blind source separation, e.g., independent component analysis (ICA), to investigate changes of temporal interactions among brain regions. However, fluctuations related to spatial components over time are of interest as well. In this paper, we examine higher-order statistical dependence between pairs of spatial components, which we define as spatial functional network connectivity (sFNC), and changes of sFNC across a resting-state scan. We extract time-varying components from healthy controls and patients with schizophrenia to represent brain networks using independent vector analysis (IVA), which is an extension of ICA to multiple data sets and enables one to capture spatial variations. Based on mutual information among IVA components, we perform statistical analysis and Markov modeling to quantify the changes in spatial connectivity. Our experimental results suggest significantly more fluctuations in patient group and show that patients with schizophrenia have more variable patterns of spatial concordance primarily between the frontoparietal, cerebellar and temporal lobe regions. This study extends upon earlier studies showing temporal connectivity differences in similar areas on average by providing evidence that the dynamic spatial interplay between these regions is also impacted by schizophrenia.

  20. Improving Memory Subsystem Performance Using ViVA: Virtual Vector Architecture

    SciTech Connect

    Gebis, Joseph; Oliker, Leonid; Shalf, John; Williams, Samuel; Yelick, Katherine

    2009-01-12

    The disparity between microprocessor clock frequencies and memory latency is a primary reason why many demanding applications run well below peak achievable performance. Software controlled scratchpad memories, such as the Cell local store, attempt to ameliorate this discrepancy by enabling precise control over memory movement; however, scratchpad technology confronts the programmer and compiler with an unfamiliar and difficult programming model. In this work, we present the Virtual Vector Architecture (ViVA), which combines the memory semantics of vector computers with a software-controlled scratchpad memory in order to provide a more effective and practical approach to latency hiding. ViVA requires minimal changes to the core design and could thus be easily integrated with conventional processor cores. To validate our approach, we implemented ViVA on the Mambo cycle-accurate full system simulator, which was carefully calibrated to match the performance on our underlying PowerPC Apple G5 architecture. Results show that ViVA is able to deliver significant performance benefits over scalar techniques for a variety of memory access patterns as well as two important memory-bound compact kernels, corner turn and sparse matrix-vector multiplication -- achieving 2x-13x improvement compared the scalar version. Overall, our preliminary ViVA exploration points to a promising approach for improving application performance on leading microprocessors with minimal design and complexity costs, in a power efficient manner.

  1. Performance of wireless sensor networks under random node failures

    SciTech Connect

    Bradonjic, Milan; Hagberg, Aric; Feng, Pan

    2011-01-28

    Networks are essential to the function of a modern society and the consequence of damages to a network can be large. Assessing network performance of a damaged network is an important step in network recovery and network design. Connectivity, distance between nodes, and alternative routes are some of the key indicators to network performance. In this paper, random geometric graph (RGG) is used with two types of node failure, uniform failure and localized failure. Since the network performance are multi-facet and assessment can be time constrained, we introduce four measures, which can be computed in polynomial time, to estimate performance of damaged RGG. Simulation experiments are conducted to investigate the deterioration of networks through a period of time. With the empirical results, the performance measures are analyzed and compared to provide understanding of different failure scenarios in a RGG.

  2. Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

    ERIC Educational Resources Information Center

    Chen, Chau-Kuang

    2010-01-01

    Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

  3. Evaluation models for soil nutrient based on support vector machine and artificial neural networks.

    PubMed

    Li, Hao; Leng, Weijia; Zhou, Yibing; Chen, Fudi; Xiu, Zhilong; Yang, Dazuo

    2014-01-01

    Soil nutrient is an important aspect that contributes to the soil fertility and environmental effects. Traditional evaluation approaches of soil nutrient are quite hard to operate, making great difficulties in practical applications. In this paper, we present a series of comprehensive evaluation models for soil nutrient by using support vector machine (SVM), multiple linear regression (MLR), and artificial neural networks (ANNs), respectively. We took the content of organic matter, total nitrogen, alkali-hydrolysable nitrogen, rapidly available phosphorus, and rapidly available potassium as independent variables, while the evaluation level of soil nutrient content was taken as dependent variable. Results show that the average prediction accuracies of SVM models are 77.87% and 83.00%, respectively, while the general regression neural network (GRNN) model's average prediction accuracy is 92.86%, indicating that SVM and GRNN models can be used effectively to assess the levels of soil nutrient with suitable dependent variables. In practical applications, both SVM and GRNN models can be used for determining the levels of soil nutrient.

  4. Performance of an integrated network model

    PubMed Central

    Lehmann, François; Dunn, David; Beaulieu, Marie-Dominique; Brophy, James

    2016-01-01

    Objective To evaluate the changes in accessibility, patients’ care experiences, and quality-of-care indicators following a clinic’s transformation into a fully integrated network clinic. Design Mixed-methods study. Setting Verdun, Que. Participants Data on all patient visits were used, in addition to 2 distinct patient cohorts: 134 patients with chronic illness (ie, diabetes, arteriosclerotic heart disease, or both); and 450 women between the ages of 20 and 70 years. Main outcome measures Accessibility was measured by the number of walk-in visits, scheduled visits, and new patient enrolments. With the first cohort, patients’ care experiences were measured using validated serial questionnaires; and quality-of-care indicators were measured using biologic data. With the second cohort, quality of preventive care was measured using the number of Papanicolaou tests performed as a surrogate marker. Results Despite a negligible increase in the number of physicians, there was an increase in accessibility after the clinic’s transition to an integrated network model. During the first 4 years of operation, the number of scheduled visits more than doubled, nonscheduled visits (walk-in visits) increased by 29%, and enrolment of vulnerable patients (those with chronic illnesses) at the clinic remained high. Patient satisfaction with doctors was rated very highly at all points of time that were evaluated. While the number of Pap tests done did not increase with time, the proportion of patients meeting hemoglobin A1c and low-density lipoprotein guideline target levels increased, as did the number of patients tested for microalbuminuria. Conclusion Transformation to an integrated network model of care led to increased efficiency and enhanced accessibility with no negative effects on the doctor-patient relationship. Improvements in biologic data also suggested better quality of care. PMID:27521410

  5. Online monitoring and control of particle size in the grinding process using least square support vector regression and resilient back propagation neural network.

    PubMed

    Pani, Ajaya Kumar; Mohanta, Hare Krishna

    2015-05-01

    Particle size soft sensing in cement mills will be largely helpful in maintaining desired cement fineness or Blaine. Despite the growing use of vertical roller mills (VRM) for clinker grinding, very few research work is available on VRM modeling. This article reports the design of three types of feed forward neural network models and least square support vector regression (LS-SVR) model of a VRM for online monitoring of cement fineness based on mill data collected from a cement plant. In the data pre-processing step, a comparative study of the various outlier detection algorithms has been performed. Subsequently, for model development, the advantage of algorithm based data splitting over random selection is presented. The training data set obtained by use of Kennard-Stone maximal intra distance criterion (CADEX algorithm) was used for development of LS-SVR, back propagation neural network, radial basis function neural network and generalized regression neural network models. Simulation results show that resilient back propagation model performs better than RBF network, regression network and LS-SVR model. Model implementation has been done in SIMULINK platform showing the online detection of abnormal data and real time estimation of cement Blaine from the knowledge of the input variables. Finally, closed loop study shows how the model can be effectively utilized for maintaining cement fineness at desired value.

  6. Non-metallic coating thickness prediction using artificial neural network and support vector machine with time resolved thermography

    NASA Astrophysics Data System (ADS)

    Wang, Hongjin; Hsieh, Sheng-Jen; Peng, Bo; Zhou, Xunfei

    2016-07-01

    A method without requirements on knowledge about thermal properties of coatings or those of substrates will be interested in the industrial application. Supervised machine learning regressions may provide possible solution to the problem. This paper compares the performances of two regression models (artificial neural networks (ANN) and support vector machines for regression (SVM)) with respect to coating thickness estimations made based on surface temperature increments collected via time resolved thermography. We describe SVM roles in coating thickness prediction. Non-dimensional analyses are conducted to illustrate the effects of coating thicknesses and various factors on surface temperature increments. It's theoretically possible to correlate coating thickness with surface increment. Based on the analyses, the laser power is selected in such a way: during the heating, the temperature increment is high enough to determine the coating thickness variance but low enough to avoid surface melting. Sixty-one pain-coated samples with coating thicknesses varying from 63.5 μm to 571 μm are used to train models. Hyper-parameters of the models are optimized by 10-folder cross validation. Another 28 sets of data are then collected to test the performance of the three methods. The study shows that SVM can provide reliable predictions of unknown data, due to its deterministic characteristics, and it works well when used for a small input data group. The SVM model generates more accurate coating thickness estimates than the ANN model.

  7. A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer

    NASA Astrophysics Data System (ADS)

    Yoon, Heesung; Jun, Seong-Chun; Hyun, Yunjung; Bae, Gwang-Ok; Lee, Kang-Kun

    2011-01-01

    SummaryWe have developed two nonlinear time-series models for predicting groundwater level (GWL) fluctuations using artificial neural networks (ANNs) and support vector machines (SVMs). The models were applied to GWL prediction of two wells at a coastal aquifer in Korea. Among the possible variables (past GWL, precipitation, and tide level) for an input structure, the past GWL was the most effective input variable for this study site. Tide level was more frequently selected as an input variable than precipitation. The results of the model performance show that root mean squared error (RMSE) values of ANN models are lower than those of SVM in model training and testing stages. However, the overall model performance criteria of the SVM are similar to or even better than those of the ANN in model prediction stage. The generalization ability of a SVM model is superior to an ANN model for input structures and lead times. The uncertainty analysis for model parameters detects an equifinality of model parameter sets and higher uncertainty for ANN model than SVM in this case. These results imply that the model-building process should be carefully conducted, especially when using ANN models for GWL forecasting in a coastal aquifer.

  8. Static internal performance including thrust vectoring and reversing of two-dimensional convergent-divergent nozzles

    NASA Technical Reports Server (NTRS)

    Re, R. J.; Leavitt, L. D.

    1984-01-01

    The effects of geometric design parameters on two dimensional convergent-divergent nozzles were investigated at nozzle pressure ratios up to 12 in the static test facility. Forward flight (dry and afterburning power settings), vectored-thrust (afterburning power setting), and reverse-thrust (dry power setting) nozzles were investigated. The nozzles had thrust vector angles from 0 deg to 20.26 deg, throat aspect ratios of 3.696 to 7.612, throat radii from sharp to 2.738 cm, expansion ratios from 1.089 to 1.797, and various sidewall lengths. The results indicate that unvectored two dimensional convergent-divergent nozzles have static internal performance comparable to axisymmetric nozzles with similar expansion ratios.

  9. Effects of Cavity on the Performance of Dual Throat Nozzle During the Thrust-Vectoring Starting Transient Process.

    PubMed

    Gu, Rui; Xu, Jinglei

    2014-01-01

    The dual throat nozzle (DTN) technique is capable to achieve higher thrust-vectoring efficiencies than other fluidic techniques, without compromising thrust efficiency significantly during vectoring operation. The excellent performance of the DTN is mainly due to the concaved cavity. In this paper, two DTNs of different scales have been investigated by unsteady numerical simulations to compare the parameter variations and study the effects of cavity during the vector starting process. The results remind us that during the vector starting process, dynamic loads may be generated, which is a potentially challenging problem for the aircraft trim and control.

  10. Impact of Trust on Security and Performance in Tactical Networks

    DTIC Science & Technology

    2013-06-01

    conditions of networks to maximize performance in several networking applications. 1 Introduction Tactical networks have been designed and operated with...based approaches to augment traditional networking methods allows one to exploit the multi- genre aspects of the problem. In this paper, we propose our...In addition, Section 4 address how trust can be modeled in a different domain and a multi-domain dealing with multi- genre networks. Section 5 describes

  11. Estimating urban impervious surfaces from Landsat-5 TM imagery using multilayer perceptron neural network and support vector machine

    NASA Astrophysics Data System (ADS)

    Sun, Zhongchang; Guo, Huadong; Li, Xinwu; Lu, Linlin; Du, Xiaoping

    2011-01-01

    In recent years, the urban impervious surface has been recognized as a key quantifiable indicator in assessing urbanization impacts on environmental and ecological conditions. A surge of research interests has resulted in the estimation of urban impervious surface using remote sensing studies. The objective of this paper is to examine and compare the effectiveness of two algorithms for extracting impervious surfaces from Landsat TM imagery; the multilayer perceptron neural network (MLPNN) and the support vector machine (SVM). An accuracy assessment was performed using the high-resolution WorldView images. The root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R2) were calculated to validate the classification performance and accuracies of MLPNN and SVM. For the MLPNN model, the RMSE, MAE, and R2 were 17.18%, 11.10%, and 0.8474, respectively. The SVM yielded a result with an RMSE of 13.75%, an MAE of 8.92%, and an R2 of 0.9032. The results indicated that SVM performance was superior to that of MLPNN in impervious surface classification. To further evaluate the performance of MLPNN and SVM in handling the mixed-pixels, an accuracy assessment was also conducted for the selected test areas, including commercial, residential, and rural areas. Our results suggested that SVM had better capability in handling the mixed-pixel problem than MLPNN. The superior performance of SVM over MLPNN is mainly attributed to the SVM's capability of deriving the global optimum and handling the over-fitting problem by suitable parameter selection. Overall, SVM provides an efficient and useful method for estimating the impervious surface.

  12. Note: Vector network analyzer-ferromagnetic resonance spectrometer using high Q-factor cavity.

    PubMed

    Lo, C K; Lai, W C; Cheng, J C

    2011-08-01

    A ferromagnetic resonance (FMR) spectrometer whose main components consist of an X-band resonator and a vector network analyzer (VNA) was developed. This spectrometer takes advantage of a high Q-factor (9600) cavity and state-of-the-art VNA. Accordingly, field modulation lock-in technique for signal to noise ratio (SNR) enhancement is no longer necessary, and FMR absorption can therefore be extracted directly. Its derivative for the ascertainment of full width at half maximum height of FMR peak can be found by taking the differentiation of original data. This system was characterized with different thicknesses of permalloy (Py) films and its multilayer, and found that the SNR of 5 nm Py on glass was better than 50, and did not have significant reduction even at low microwave excitation power (-20 dBm), and at low Q-factor (3000). The FMR other than X-band can also be examined in the same manner by using a suitable band cavity within the frequency range of VNA.

  13. Note: Vector network analyzer-ferromagnetic resonance spectrometer using high Q-factor cavity

    NASA Astrophysics Data System (ADS)

    Lo, C. K.; Lai, W. C.; Cheng, J. C.

    2011-08-01

    A ferromagnetic resonance (FMR) spectrometer whose main components consist of an X-band resonator and a vector network analyzer (VNA) was developed. This spectrometer takes advantage of a high Q-factor (9600) cavity and state-of-the-art VNA. Accordingly, field modulation lock-in technique for signal to noise ratio (SNR) enhancement is no longer necessary, and FMR absorption can therefore be extracted directly. Its derivative for the ascertainment of full width at half maximum height of FMR peak can be found by taking the differentiation of original data. This system was characterized with different thicknesses of permalloy (Py) films and its multilayer, and found that the SNR of 5 nm Py on glass was better than 50, and did not have significant reduction even at low microwave excitation power (-20 dBm), and at low Q-factor (3000). The FMR other than X-band can also be examined in the same manner by using a suitable band cavity within the frequency range of VNA.

  14. Sensor network based solar forecasting using a local vector autoregressive ridge framework

    SciTech Connect

    Xu, J.; Yoo, S.; Heiser, J.; Kalb, P.

    2016-04-04

    The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations due to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset.

  15. An Improved Ensemble of Random Vector Functional Link Networks Based on Particle Swarm Optimization with Double Optimization Strategy.

    PubMed

    Ling, Qing-Hua; Song, Yu-Qing; Han, Fei; Yang, Dan; Huang, De-Shuang

    2016-01-01

    For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is one of suitable base-classifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In this paper, to obtain a more compact ensemble system with improved convergence performance, an improved ensemble of RVFL based on attractive and repulsive particle swarm optimization (ARPSO) with double optimization strategy is proposed. In the proposed method, ARPSO is applied to select and combine the candidate RVFL. As for using ARPSO to select the optimal base RVFL, ARPSO considers both the convergence accuracy on the validation data and the diversity of the candidate ensemble system to build the RVFL ensembles. In the process of combining RVFL, the ensemble weights corresponding to the base RVFL are initialized by the minimum norm least-square method and then further optimized by ARPSO. Finally, a few redundant RVFL is pruned, and thus the more compact ensemble of RVFL is obtained. Moreover, in this paper, theoretical analysis and justification on how to prune the base classifiers on classification problem is presented, and a simple and practically feasible strategy for pruning redundant base classifiers on both classification and regression problems is proposed. Since the double optimization is performed on the basis of the single optimization, the ensemble of RVFL built by the proposed method outperforms that built by some single optimization methods. Experiment results on function approximation and classification problems verify that the proposed method could improve its convergence accuracy as well as reduce the complexity of the ensemble system.

  16. An Improved Ensemble of Random Vector Functional Link Networks Based on Particle Swarm Optimization with Double Optimization Strategy

    PubMed Central

    Ling, Qing-Hua; Song, Yu-Qing; Han, Fei; Yang, Dan; Huang, De-Shuang

    2016-01-01

    For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is one of suitable base-classifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In this paper, to obtain a more compact ensemble system with improved convergence performance, an improved ensemble of RVFL based on attractive and repulsive particle swarm optimization (ARPSO) with double optimization strategy is proposed. In the proposed method, ARPSO is applied to select and combine the candidate RVFL. As for using ARPSO to select the optimal base RVFL, ARPSO considers both the convergence accuracy on the validation data and the diversity of the candidate ensemble system to build the RVFL ensembles. In the process of combining RVFL, the ensemble weights corresponding to the base RVFL are initialized by the minimum norm least-square method and then further optimized by ARPSO. Finally, a few redundant RVFL is pruned, and thus the more compact ensemble of RVFL is obtained. Moreover, in this paper, theoretical analysis and justification on how to prune the base classifiers on classification problem is presented, and a simple and practically feasible strategy for pruning redundant base classifiers on both classification and regression problems is proposed. Since the double optimization is performed on the basis of the single optimization, the ensemble of RVFL built by the proposed method outperforms that built by some single optimization methods. Experiment results on function approximation and classification problems verify that the proposed method could improve its convergence accuracy as well as reduce the complexity of the ensemble system. PMID:27835638

  17. Efficient modeling of vector hysteresis using a novel Hopfield neural network implementation of Stoner–Wohlfarth-like operators

    PubMed Central

    Adly, Amr A.; Abd-El-Hafiz, Salwa K.

    2012-01-01

    Incorporation of hysteresis models in electromagnetic analysis approaches is indispensable to accurate field computation in complex magnetic media. Throughout those computations, vector nature and computational efficiency of such models become especially crucial when sophisticated geometries requiring massive sub-region discretization are involved. Recently, an efficient vector Preisach-type hysteresis model constructed from only two scalar models having orthogonally coupled elementary operators has been proposed. This paper presents a novel Hopfield neural network approach for the implementation of Stoner–Wohlfarth-like operators that could lead to a significant enhancement in the computational efficiency of the aforementioned model. Advantages of this approach stem from the non-rectangular nature of these operators that substantially minimizes the number of operators needed to achieve an accurate vector hysteresis model. Details of the proposed approach, its identification and experimental testing are presented in the paper. PMID:25685446

  18. Efficient modeling of vector hysteresis using a novel Hopfield neural network implementation of Stoner-Wohlfarth-like operators.

    PubMed

    Adly, Amr A; Abd-El-Hafiz, Salwa K

    2013-07-01

    Incorporation of hysteresis models in electromagnetic analysis approaches is indispensable to accurate field computation in complex magnetic media. Throughout those computations, vector nature and computational efficiency of such models become especially crucial when sophisticated geometries requiring massive sub-region discretization are involved. Recently, an efficient vector Preisach-type hysteresis model constructed from only two scalar models having orthogonally coupled elementary operators has been proposed. This paper presents a novel Hopfield neural network approach for the implementation of Stoner-Wohlfarth-like operators that could lead to a significant enhancement in the computational efficiency of the aforementioned model. Advantages of this approach stem from the non-rectangular nature of these operators that substantially minimizes the number of operators needed to achieve an accurate vector hysteresis model. Details of the proposed approach, its identification and experimental testing are presented in the paper.

  19. Analysis of complex network performance and heuristic node removal strategies

    NASA Astrophysics Data System (ADS)

    Jahanpour, Ehsan; Chen, Xin

    2013-12-01

    Removing important nodes from complex networks is a great challenge in fighting against criminal organizations and preventing disease outbreaks. Six network performance metrics, including four new metrics, are applied to quantify networks' diffusion speed, diffusion scale, homogeneity, and diameter. In order to efficiently identify nodes whose removal maximally destroys a network, i.e., minimizes network performance, ten structured heuristic node removal strategies are designed using different node centrality metrics including degree, betweenness, reciprocal closeness, complement-derived closeness, and eigenvector centrality. These strategies are applied to remove nodes from the September 11, 2001 hijackers' network, and their performance are compared to that of a random strategy, which removes randomly selected nodes, and the locally optimal solution (LOS), which removes nodes to minimize network performance at each step. The computational complexity of the 11 strategies and LOS is also analyzed. Results show that the node removal strategies using degree and betweenness centralities are more efficient than other strategies.

  20. Wireless Local Area Network Performance Inside Aircraft Passenger Cabins

    NASA Technical Reports Server (NTRS)

    Whetten, Frank L.; Soroker, Andrew; Whetten, Dennis A.; Whetten, Frank L.; Beggs, John H.

    2005-01-01

    An examination of IEEE 802.11 wireless network performance within an aircraft fuselage is performed. This examination measured the propagated RF power along the length of the fuselage, and the associated network performance: the link speed, total throughput, and packet losses and errors. A total of four airplanes: one single-aisle and three twin-aisle airplanes were tested with 802.11a, 802.11b, and 802.11g networks.

  1. Performance characteristics of a one-third-scale, vectorable ventral nozzle for SSTOVL aircraft

    NASA Technical Reports Server (NTRS)

    Esker, Barbara S.; Mcardle, Jack G.

    1990-01-01

    Several proposed configurations for supersonic short takeoff, vertical landing aircraft will require one or more ventral nozzles for lift and pitch control. The swivel nozzle is one possible ventral nozzle configuration. A swivel nozzle (approximately one-third scale) was built and tested on a generic model tailpipe. This nozzle was capable of vectoring the flow up to + or - 23 deg from the vertical position. Steady-state performance data were obtained at pressure ratios to 4.5, and pitot-pressure surveys of the nozzle exit plane were made. Two configurations were tested: the swivel nozzle with a square contour of the leading edge of the ventral duct inlet, and the same nozzle with a round leading edge contour. The swivel nozzle showed good performance overall, and the round-leading edge configuration showed an improvement in performance over the square-leading edge configuration.

  2. Using adaline neural network for performance improvement of smart antennas in TDD wireless communications.

    PubMed

    Kavak, Adnan; Yigit, Halil; Ertunc, H Metin

    2005-11-01

    In time-division-duplex (TDD) mode wireless communications, downlink beamforming performance of a smart antenna system at the base station can be degraded due to variation of spatial signature vectors corresponding to mobile users especially in fast fading scenarios. To mitigate this, downlink beams must be controlled by properly adjusting their weight vectors in response to changing propagation dynamics. This can be achieved by modeling the spatial signature vectors in the uplink period and then predicting them to be used as beamforming weight vectors for the new mobile position in the downlink transmission period. We show that ADAptive LInear NEuron (ADALINE) network modeling based prediction of spatial signatures provides certain level of performance improvement compared to conventional beamforming method that employs spatial signature obtained in previous uplink interval. We compare the performance of ADALINE with autoregressive (AR) modeling based predictions under varying channel propagation (mobile speed, multipath angle spread, and number of multipaths), and filter order/delay conditions. ADALINE modeling outperforms AR modeling in terms of downlink SNR improvement and relative error improvement especially under high mobile speeds, i.e., V = 100 km/h.

  3. Supporting performance and configuration management of GTE cellular networks

    SciTech Connect

    Tan, Ming; Lafond, C.; Jakobson, G.; Young, G.

    1996-12-31

    GTE Laboratories, in cooperation with GTE Mobilnet, has developed and deployed PERFFEX (PERFormance Expert), an intelligent system for performance and configuration management of cellular networks. PERFEX assists cellular network performance and radio engineers in the analysis of large volumes of cellular network performance and configuration data. It helps them locate and determine the probable causes of performance problems, and provides intelligent suggestions about how to correct them. The system combines an expert cellular network performance tuning capability with a map-based graphical user interface, data visualization programs, and a set of special cellular engineering tools. PERFEX is in daily use at more than 25 GTE Mobile Switching Centers. Since the first deployment of the system in late 1993, PERFEX has become a major GTE cellular network performance optimization tool.

  4. Improving matrix-vector product performance and multi-level preconditioning for the parallel PCG package

    SciTech Connect

    McLay, R.T.; Carey, G.F.

    1996-12-31

    In this study we consider parallel solution of sparse linear systems arising from discretized PDE`s. As part of our continuing work on our parallel PCG Solver package, we have made improvements in two areas. The first is improving the performance of the matrix-vector product. Here on regular finite-difference grids, we are able to use the cache memory more efficiently for smaller domains or where there are multiple degrees of freedom. The second problem of interest in the present work is the construction of preconditioners in the context of the parallel PCG solver we are developing. Here the problem is partitioned over a set of processors subdomains and the matrix-vector product for PCG is carried out in parallel for overlapping grid subblocks. For problems of scaled speedup, the actual rate of convergence of the unpreconditioned system deteriorates as the mesh is refined. Multigrid and subdomain strategies provide a logical approach to resolving the problem. We consider the parallel trade-offs between communication and computation and provide a complexity analysis of a representative algorithm. Some preliminary calculations using the parallel package and comparisons with other preconditioners are provided together with parallel performance results.

  5. Performance Evaluation of Plasma and Astrophysics Applications onModern Parallel Vector Systems

    SciTech Connect

    Carter, Jonathan; Oliker, Leonid; Shalf, John

    2005-10-28

    The last decade has witnessed a rapid proliferation ofsuperscalar cache-based microprocessors to build high-endcomputing (HEC)platforms, primarily because of their generality,scalability, and costeffectiveness. However, the growing gap between sustained and peakperformance for full-scale scientific applications on such platforms hasbecome major concern in highperformance computing. The latest generationof custom-built parallel vector systems have the potential to addressthis concern for numerical algorithms with sufficient regularity in theircomputational structure. In this work, we explore two and threedimensional implementations of a plasma physics application, as well as aleading astrophysics package on some of today's most powerfulsupercomputing platforms. Results compare performance between the thevector-based Cray X1, EarthSimulator, and newly-released NEC SX- 8, withthe commodity-based superscalar platforms of the IBM Power3, IntelItanium2, and AMDOpteron. Overall results show that the SX-8 attainsunprecedented aggregate performance across our evaluatedapplications.

  6. Performance of a novel micro force vector sensor and outlook into its biomedical applications

    NASA Astrophysics Data System (ADS)

    Meiss, Thorsten; Rossner, Tim; Minamisava Faria, Carlos; Völlmeke, Stefan; Opitz, Thomas; Werthschützky, Roland

    2011-05-01

    For the HapCath system, which provides haptic feedback of the forces acting on a guide wire's tip during vascular catheterization, very small piezoresistive force sensors of 200•200•640μm3 have been developed. This paper focuses on the characterization of the measurement performance and on possible new applications. Besides the determination of the dynamic measurement performance, special focus is put onto the results of the 3- component force vector calibration. This article addresses special advantageous characteristics of the sensor, but also the limits of applicability will be addressed. As for the special characteristics of the sensor, the second part of the article demonstrates new applications which can be opened up with the novel force sensor, like automatic navigation of medical or biological instruments without impacting surrounding tissue, surface roughness evaluation in biomedical systems, needle insertion with tactile or higher level feedback, or even building tactile hairs for artificial organisms.

  7. Performance characteristics of two multiaxis thrust-vectoring nozzles at Mach numbers up to 1.28

    NASA Technical Reports Server (NTRS)

    Wing, David J.; Capone, Francis J.

    1993-01-01

    The thrust-vectoring axisymmetric (VA) nozzle and a spherical convergent flap (SCF) thrust-vectoring nozzle were tested along with a baseline nonvectoring axisymmetric (NVA) nozzle in the Langley 16-Foot Transonic Tunnel at Mach numbers from 0 to 1.28 and nozzle pressure ratios from 1 to 8. Test parameters included geometric yaw vector angle and unvectored divergent flap length. No pitch vectoring was studied. Nozzle drag, thrust minus drag, yaw thrust vector angle, discharge coefficient, and static thrust performance were measured and analyzed, as well as external static pressure distributions. The NVA nozzle and the VA nozzle displayed higher static thrust performance than the SCF nozzle throughout the nozzle pressure ratio (NPR) range tested. The NVA nozzle had higher overall thrust minus drag than the other nozzles throughout the NPR and Mach number ranges tested. The SCF nozzle had the lowest jet-on nozzle drag of the three nozzles throughout the test conditions. The SCF nozzle provided yaw thrust angles that were equal to the geometric angle and constant with NPR. The VA nozzle achieved yaw thrust vector angles that were significantly higher than the geometric angle but not constant with NPR. Nozzle drag generally increased with increases in thrust vectoring for all the nozzles tested.

  8. Network interface unit design options performance analysis

    NASA Technical Reports Server (NTRS)

    Miller, Frank W.

    1991-01-01

    An analysis is presented of three design options for the Space Station Freedom (SSF) onboard Data Management System (DMS) Network Interface Unit (NIU). The NIU provides the interface from the Fiber Distributed Data Interface (FDDI) local area network (LAN) to the DMS processing elements. The FDDI LAN provides the primary means for command and control and low and medium rate telemetry data transfers on board the SSF. The results of this analysis provide the basis for the implementation of the NIU.

  9. On the MAC/Network/Energy Performance Evaluation of Wireless Sensor Networks: Contrasting MPH, AODV, DSR and ZTR Routing Protocols

    PubMed Central

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

    2014-01-01

    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 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. PMID:25474377

  10. 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.

  11. Performance Enhancement for a GPS Vector-Tracking Loop Utilizing an Adaptive Iterated Extended Kalman Filter

    PubMed Central

    Chen, Xiyuan; Wang, Xiying; Xu, Yuan

    2014-01-01

    This paper deals with the problem of state estimation for the vector-tracking loop of a software-defined Global Positioning System (GPS) receiver. For a nonlinear system that has the model error and white Gaussian noise, a noise statistics estimator is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) named adaptive iterated Kalman filter (AIEKF) is proposed. A vector-tracking GPS receiver utilizing AIEKF is implemented to evaluate the performance of the proposed method. Through road tests, it is shown that the proposed method has an obvious accuracy advantage over the IEKF and Adaptive Extended Kalman filter (AEKF) in position determination. The results show that the proposed method is effective to reduce the root-mean-square error (RMSE) of position (including longitude, latitude and altitude). Comparing with EKF, the position RMSE values of AIEKF are reduced by about 45.1%, 40.9% and 54.6% in the east, north and up directions, respectively. Comparing with IEKF, the position RMSE values of AIEKF are reduced by about 25.7%, 19.3% and 35.7% in the east, north and up directions, respectively. Compared with AEKF, the position RMSE values of AIEKF are reduced by about 21.6%, 15.5% and 30.7% in the east, north and up directions, respectively. PMID:25502124

  12. Performance evaluation of random forest and support vector regressions in natural hazard change detection

    NASA Astrophysics Data System (ADS)

    Eisavi, Vahid; Homayouni, Saeid

    2016-10-01

    Information on land use and land cover changes is considered as a foremost requirement for monitoring environmental change. Developing change detection methodology in the remote sensing community is an active research topic. However, to the best of our knowledge, no research has been conducted so far on the application of random forest regression (RFR) and support vector regression (SVR) for natural hazard change detection from high-resolution optical remote sensing observations. Hence, the objective of this study is to examine the use of RFR and SVR to discriminate between changed and unchanged areas after a tsunami. For this study, RFR and SVR were applied to two different pilot coastlines in Indonesia and Japan. Two different remotely sensed data sets acquired by Quickbird and Ikonos sensors were used for efficient evaluation of the proposed methodology. The results demonstrated better performance of SVM compared to random forest (RF) with an overall accuracy higher by 3% to 4% and kappa coefficient by 0.05 to 0.07. Using McNemar's test, statistically significant differences (Z≥1.96), at the 5% significance level, between the confusion matrices of the RF classifier and the support vector classifier were observed in both study areas. The high accuracy of change detection obtained in this study confirms that these methods have the potential to be used for detecting changes due to natural hazards.

  13. Performance enhancement for a GPS vector-tracking loop utilizing an adaptive iterated extended Kalman filter.

    PubMed

    Chen, Xiyuan; Wang, Xiying; Xu, Yuan

    2014-12-09

    This paper deals with the problem of state estimation for the vector-tracking loop of a software-defined Global Positioning System (GPS) receiver. For a nonlinear system that has the model error and white Gaussian noise, a noise statistics estimator is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) named adaptive iterated Kalman filter (AIEKF) is proposed. A vector-tracking GPS receiver utilizing AIEKF is implemented to evaluate the performance of the proposed method. Through road tests, it is shown that the proposed method has an obvious accuracy advantage over the IEKF and Adaptive Extended Kalman filter (AEKF) in position determination. The results show that the proposed method is effective to reduce the root-mean-square error (RMSE) of position (including longitude, latitude and altitude). Comparing with EKF, the position RMSE values of AIEKF are reduced by about 45.1%, 40.9% and 54.6% in the east, north and up directions, respectively. Comparing with IEKF, the position RMSE values of AIEKF are reduced by about 25.7%, 19.3% and 35.7% in the east, north and up directions, respectively. Compared with AEKF, the position RMSE values of AIEKF are reduced by about 21.6%, 15.5% and 30.7% in the east, north and up directions, respectively.

  14. Building and measuring a high performance network architecture

    SciTech Connect

    Kramer, William T.C.; Toole, Timothy; Fisher, Chuck; Dugan, Jon; Wheeler, David; Wing, William R; Nickless, William; Goddard, Gregory; Corbato, Steven; Love, E. Paul; Daspit, Paul; Edwards, Hal; Mercer, Linden; Koester, David; Decina, Basil; Dart, Eli; Paul Reisinger, Paul; Kurihara, Riki; Zekauskas, Matthew J; Plesset, Eric; Wulf, Julie; Luce, Douglas; Rogers, James; Duncan, Rex; Mauth, Jeffery

    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. 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.

  15. Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer

    PubMed Central

    Gutiérrez, Salvador; Tardaguila, Javier; Fernández-Novales, Juan; Diago, María P.

    2015-01-01

    The identification of different grapevine varieties, currently attended using visual ampelometry, DNA analysis and very recently, by hyperspectral analysis under laboratory conditions, is an issue of great importance in the wine industry. This work presents support vector machine and artificial neural network’s modelling for grapevine varietal classification from in-field leaf spectroscopy. Modelling was attempted at two scales: site-specific and a global scale. Spectral measurements were obtained on the near-infrared (NIR) spectral range between 1600 to 2400 nm under field conditions in a non-destructive way using a portable spectrophotometer. For the site specific approach, spectra were collected from the adaxial side of 400 individual leaves of 20 grapevine (Vitis vinifera L.) varieties one week after veraison. For the global model, two additional sets of spectra were collected one week before harvest from two different vineyards in another vintage, each one consisting on 48 measurement from individual leaves of six varieties. Several combinations of spectra scatter correction and smoothing filtering were studied. For the training of the models, support vector machines and artificial neural networks were employed using the pre-processed spectra as input and the varieties as the classes of the models. The results from the pre-processing study showed that there was no influence whether using scatter correction or not. Also, a second-degree derivative with a window size of 5 Savitzky-Golay filtering yielded the highest outcomes. For the site-specific model, with 20 classes, the best results from the classifiers thrown an overall score of 87.25% of correctly classified samples. These results were compared under the same conditions with a model trained using partial least squares discriminant analysis, which showed a worse performance in every case. For the global model, a 6-class dataset involving samples from three different vineyards, two years and leaves

  16. Performance Statistics of the DWD Ceilometer Network

    NASA Astrophysics Data System (ADS)

    Wagner, Frank; Mattis, Ina; Flentje, Harald; Thomas, Werner

    2015-04-01

    The DWD ceilometer network was created in 2008. In the following years more and more ceilometers of type CHM15k (manufacturer Jenoptik) were installed with the aim of observing atmospheric aerosol particles. Now, 58 ceilometers are in continuous operation. The presentation aims on the one side on the statistical behavior of a several instrumental parameters which are related to the measurement performance. Some problems are addressed and conclusions or recommendations which parameters should be monitored for unattended automated operation. On the other side, the presentation aims on a statistical analysis of several measured quantities. Differences between geographic locations (e.g. north versus south, mountainous versus flat terrain) are investigated. For instance the occurrence of fog in lowlands is associated with the overall meteorological situation whereas mountain stations such as Hohenpeissenberg are often within a cumulus cloud which appears as fog in the measurements. The longest time series of data were acquired at Lindenberg. The ceilometer was installed in 2008. Until the end of 2008 the number of installed ceilometers increased to 28 and in the end of 2009 already 42 instruments were measuring. In 2011 the ceilometers were upgraded to the so-called Nimbus instruments. The nimbus instruments have enhanced capabilities of coping and correcting short-term instrumental fluctuations (e.g. detector sensitivity). About 30% of all ceilometer measurements were done under clear skies and hence can be used without limitations for aerosol particle observations. Multiple cloud layers could only be detected in about 23% of all cases with clouds. This is caused either by the presence of only 1 cloud layer or that the ceilometer laser beam could not see through the lowest cloud and hence was blind for the detection of several cloud layers. 3 cloud layers could only be detected in 5% of all cases with clouds. Considering only cases without clouds the diurnal cycle for

  17. A parallel-vector algorithm for rapid structural analysis on high-performance computers

    NASA Technical Reports Server (NTRS)

    Storaasli, Olaf O.; Nguyen, Duc T.; Agarwal, Tarun K.

    1990-01-01

    A fast, accurate Choleski method for the solution of symmetric systems of linear equations is presented. This direct method is based on a variable-band storage scheme and takes advantage of column heights to reduce the number of operations in the Choleski factorization. The method employs parallel computation in the outermost DO-loop and vector computation via the loop unrolling technique in the innermost DO-loop. The method avoids computations with zeros outside the column heights, and as an option, zeros inside the band. The close relationship between Choleski and Gauss elimination methods is examined. The minor changes required to convert the Choleski code to a Gauss code to solve non-positive-definite symmetric systems of equations are identified. The results for two large scale structural analyses performed on supercomputers, demonstrate the accuracy and speed of the method.

  18. A parallel-vector algorithm for rapid structural analysis on high-performance computers

    NASA Technical Reports Server (NTRS)

    Storaasli, Olaf O.; Nguyen, Duc T.; Agarwal, Tarun K.

    1990-01-01

    A fast, accurate Choleski method for the solution of symmetric systems of linear equations is presented. This direct method is based on a variable-band storage scheme and takes advantage of column heights to reduce the number of operations in the Choleski factorization. The method employs parallel computation in the outermost DO-loop and vector computation via the 'loop unrolling' technique in the innermost DO-loop. The method avoids computations with zeros outside the column heights, and as an option, zeros inside the band. The close relationship between Choleski and Gauss elimination methods is examined. The minor changes required to convert the Choleski code to a Gauss code to solve non-positive-definite symmetric systems of equations are identified. The results for two large-scale structural analyses performed on supercomputers, demonstrate the accuracy and speed of the method.

  19. Topology design and performance analysis of an integrated communication network

    NASA Technical Reports Server (NTRS)

    Li, V. O. K.; Lam, Y. F.; Hou, T. C.; Yuen, J. H.

    1985-01-01

    A research study on the topology design and performance analysis for the Space Station Information System (SSIS) network is conducted. It is begun with a survey of existing research efforts in network topology design. Then a new approach for topology design is presented. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. The algorithm for generating subsets is described in detail, and various aspects of the overall design procedure are discussed. Two more efficient versions of this algorithm (applicable in specific situations) are also given. Next, two important aspects of network performance analysis: network reliability and message delays are discussed. A new model is introduced to study the reliability of a network with dependent failures. For message delays, a collection of formulas from existing research results is given to compute or estimate the delays of messages in a communication network without making the independence assumption. The design algorithm coded in PASCAL is included as an appendix.

  20. Network based high performance concurrent computing

    SciTech Connect

    Sunderam, V.S.

    1991-01-01

    The overall objectives of this project are to investigate research issues pertaining to programming tools and efficiency issues in network based concurrent computing systems. The basis for these efforts is the PVM project that evolved during my visits to Oak Ridge Laboratories under the DOE Faculty Research Participation program; I continue to collaborate with researchers at Oak Ridge on some portions of the project.

  1. Identifying cysteines and histidines in transition-metal-binding sites using support vector machines and neural networks.

    PubMed

    Passerini, Andrea; Punta, Marco; Ceroni, Alessio; Rost, Burkhard; Frasconi, Paolo

    2006-11-01

    Accurate predictions of metal-binding sites in proteins by using sequence as the only source of information can significantly help in the prediction of protein structure and function, genome annotation, and in the experimental determination of protein structure. Here, we introduce a method for identifying histidines and cysteines that participate in binding of several transition metals and iron complexes. The method predicts histidines as being in either of two states (free or metal bound) and cysteines in either of three states (free, metal bound, or in disulfide bridges). The method uses only sequence information by utilizing position-specific evolutionary profiles as well as more global descriptors such as protein length and amino acid composition. Our solution is based on a two-stage machine-learning approach. The first stage consists of a support vector machine trained to locally classify the binding state of single histidines and cysteines. The second stage consists of a bidirectional recurrent neural network trained to refine local predictions by taking into account dependencies among residues within the same protein. A simple finite state automaton is employed as a postprocessing in the second stage in order to enforce an even number of disulfide-bonded cysteines. We predict histidines and cysteines in transition-metal-binding sites at 73% precision and 61% recall. We observe significant differences in performance depending on the ligand (histidine or cysteine) and on the metal bound. We also predict cysteines participating in disulfide bridges at 86% precision and 87% recall. Results are compared to those that would be obtained by using expert information as represented by PROSITE motifs and, for disulfide bonds, to state-of-the-art methods.

  2. End-to-end network/application performance troubleshooting methodology

    SciTech Connect

    Wu, Wenji; Bobyshev, Andrey; Bowden, Mark; Crawford, Matt; Demar, Phil; Grigaliunas, Vyto; Grigoriev, Maxim; Petravick, Don; /Fermilab

    2007-09-01

    The computing models for HEP experiments are globally distributed and grid-based. Obstacles to good network performance arise from many causes and can be a major impediment to the success of the computing models for HEP experiments. Factors that affect overall network/application performance exist on the hosts themselves (application software, operating system, hardware), in the local area networks that support the end systems, and within the wide area networks. Since the computer and network systems are globally distributed, it can be very difficult to locate and identify the factors that are hurting application performance. In this paper, we present an end-to-end network/application performance troubleshooting methodology developed and in use at Fermilab. The core of our approach is to narrow down the problem scope with a divide and conquer strategy. The overall complex problem is split into two distinct sub-problems: host diagnosis and tuning, and network path analysis. After satisfactorily evaluating, and if necessary resolving, each sub-problem, we conduct end-to-end performance analysis and diagnosis. The paper will discuss tools we use as part of the methodology. The long term objective of the effort is to enable site administrators and end users to conduct much of the troubleshooting themselves, before (or instead of) calling upon network and operating system 'wizards,' who are always in short supply.

  3. Protein interaction networks at the host–microbe interface in Diaphorina citri, the insect vector of the citrus greening pathogen

    PubMed Central

    Chavez, J. D.; Johnson, R.; Hosseinzadeh, S.; Mahoney, J. E.; Mohr, J. P.; Robison, F.; Zhong, X.; Hall, D. G.; MacCoss, M.; Bruce, J.; Cilia, M.

    2017-01-01

    The Asian citrus psyllid (Diaphorina citri) is the insect vector responsible for the worldwide spread of ‘Candidatus Liberibacter asiaticus’ (CLas), the bacterial pathogen associated with citrus greening disease. Developmental changes in the insect vector impact pathogen transmission, such that D. citri transmission of CLas is more efficient when bacteria are acquired by nymphs when compared with adults. We hypothesize that expression changes in the D. citri immune system and commensal microbiota occur during development and regulate vector competency. In support of this hypothesis, more proteins, with greater fold changes, were differentially expressed in response to CLas in adults when compared with nymphs, including insect proteins involved in bacterial adhesion and immunity. Compared with nymphs, adult insects had a higher titre of CLas and the bacterial endosymbionts Wolbachia, Profftella and Carsonella. All Wolbachia and Profftella proteins differentially expressed between nymphs and adults are upregulated in adults, while most differentially expressed Carsonella proteins are upregulated in nymphs. Discovery of protein interaction networks has broad applicability to the study of host–microbe relationships. Using protein interaction reporter technology, a D. citri haemocyanin protein highly upregulated in response to CLas was found to physically interact with the CLas coenzyme A (CoA) biosynthesis enzyme phosphopantothenoylcysteine synthetase/decarboxylase. CLas pantothenate kinase, which catalyses the rate-limiting step of CoA biosynthesis, was found to interact with a D. citri myosin protein. Two Carsonella enzymes involved in histidine and tryptophan biosynthesis were found to physically interact with D. citri proteins. These co-evolved protein interaction networks at the host–microbe interface are highly specific targets for controlling the insect vector responsible for the spread of citrus greening. PMID:28386418

  4. A performance data network for solar process heat systems

    SciTech Connect

    Barker, G.; Hale, M.J.

    1996-03-01

    A solar process heat (SPH) data network has been developed to access remote-site performance data from operational solar heat systems. Each SPH system in the data network is outfitted with monitoring equipment and a datalogger. The datalogger is accessed via modem from the data network computer at the National Renewable Energy Laboratory (NREL). The dataloggers collect both ten-minute and hourly data and download it to the data network every 24-hours for archiving, processing, and plotting. The system data collected includes energy delivered (fluid temperatures and flow rates) and site meteorological conditions, such as solar insolation and ambient temperature. The SPH performance data network was created for collecting performance data from SPH systems that are serving in industrial applications or from systems using technologies that show promise for industrial applications. The network will be used to identify areas of SPH technology needing further development, to correlate computer models with actual performance, and to improve the credibility of SPH technology. The SPH data network also provides a centralized bank of user-friendly performance data that will give prospective SPH users an indication of how actual systems perform. There are currently three systems being monitored and archived under the SPH data network: two are parabolic trough systems and the third is a flat-plate system. The two trough systems both heat water for prisons; the hot water is used for personal hygiene, kitchen operations, and laundry. The flat plate system heats water for meat processing at a slaughter house. We plan to connect another parabolic trough system to the network during the first months of 1996. We continue to look for good examples of systems using other types of collector technologies and systems serving new applications (such as absorption chilling) to include in the SPH performance data network.

  5. Reduced-Complexity Models for Network Performance Prediction

    DTIC Science & Technology

    2005-05-01

    traffic over the network . To understand such a complex system it is necessary to develop accurate, yet simple, models to describe the performance...interconnected in complex ways, with millions of users sending traffic over the network . To understand such a complex system, it is necessary to develop...number of downloaders . . . . . . . . . . . . . . . . . 17 11 A network of ISP clouds. In this figure, the ISPs are connected via peering points, denoted

  6. Optimal Beamforming and Performance Analysis of Wireless Relay Networks with Unmanned Aerial Vehicle

    NASA Astrophysics Data System (ADS)

    Ouyang, Jian; Lin, Min

    2015-03-01

    In this paper, we investigate a wireless communication system employing a multi-antenna unmanned aerial vehicle (UAV) as the relay to improve the connectivity between the base station (BS) and the receive node (RN), where the BS-UAV link undergoes the correlated Rician fading while the UAV-RN link follows the correlated Rayleigh fading with large scale path loss. By assuming that the amplify-and-forward (AF) protocol is adopted at UAV, we first propose an optimal beamforming (BF) scheme to maximize the mutual information of the UAV-assisted dual-hop relay network, by calculating the BF weight vectors and the power allocation coefficient. Then, we derive the analytical expressions for the outage probability (OP) and the ergodic capacity (EC) of the relay network to evaluate the system performance conveniently. Finally, computer simulation results are provided to demonstrate the validity and efficiency of the proposed scheme as well as the performance analysis.

  7. An intercomparison of different topography effects on discrimination performance of fuzzy change vector analysis algorithm

    NASA Astrophysics Data System (ADS)

    Singh, Sartajvir; Talwar, Rajneesh

    2016-12-01

    Detection of snow cover changes is vital for avalanche hazard analysis and flood flashes that arise due to variation in temperature. Hence, multitemporal change detection is one of the practical mean to estimate the snow cover changes over larger area using remotely sensed data. There have been some previous studies that examined how accuracy of change detection analysis is affected by different topography effects over Northwestern Indian Himalayas. The present work emphases on the intercomparison of different topography effects on discrimination performance of fuzzy based change vector analysis (FCVA) as change detection algorithm that includes extraction of change-magnitude and change-direction from a specific pixel belongs multiple or partial membership. The qualitative and quantitative analysis of the proposed FCVA algorithm is performed under topographic conditions and topographic correction conditions. The experimental outcomes confirmed that in change category discrimination procedure, FCVA with topographic correction achieved 86.8% overall accuracy and 4.8% decay (82% of overall accuracy) is found in FCVA without topographic correction. This study suggests that by incorporating the topographic correction model over mountainous region satellite imagery, performance of FCVA algorithm can be significantly improved up to great extent in terms of determining actual change categories.

  8. Performance of a Regional Aeronautical Telecommunications Network

    NASA Technical Reports Server (NTRS)

    Bretmersky, Steven C.; Ripamonti, Claudio; Konangi, Vijay K.; Kerczewski, Robert J.

    2001-01-01

    This paper reports the findings of the simulation of the ATN (Aeronautical Telecommunications Network) for three typical average-sized U.S. airports and their associated air traffic patterns. The models of the protocols were designed to achieve the same functionality and meet the ATN specifications. The focus of this project is on the subnetwork and routing aspects of the simulation. To maintain continuous communication between the aircrafts and the ground facilities, a model based on mobile IP is used. The results indicate that continuous communication is indeed possible. The network can support two applications of significance in the immediate future FTP and HTTP traffic. Results from this simulation prove the feasibility of development of the ATN concept for AC/ATM (Advanced Communications for Air Traffic Management).

  9. Performance of velocity vector estimation using an improved dynamic beamforming setup

    NASA Astrophysics Data System (ADS)

    Munk, Peter; Jensen, Joergen A.

    2001-05-01

    Estimation of velocity vectors using transverse spatial modulation has previously been presented. Initially, the velocity estimation was improved using an approximated dynamic beamformer setup instead of a static combined with a new velocity estimation scheme. A new beamformer setup for dynamic control of the acoustic field, based on the Pulsed Plane Wave Decomposition (PPWD), is presented. The PPWD gives an unambiguous relation between a given acoustic field and the time functions needed on an array transducer for transmission. Applying this method for the receive beamformation results in a setup of the beamformer with different filters for each channel for each estimation depth. The method of the PPWD is illustrated by analytical expressions of the decomposed acoustic field and these results are used for simulation. Results of velocity estimates using the new setup are given on the basis of simulated and experimental data. The simulation setup is an attempt to approximate the situation present when performing a scanning of the carotid artery with a linear array. Measurement of the flow perpendicular to the emission direction is possible using the approach of transverse spatial modulation. This is most often the case in a scanning of the carotid artery, where the situation is handled by an angled Doppler setup in the present ultrasound scanners. The modulation period of 2 mm is controlled for a range of 20-40 mm which covers the typical range of the carotid artery. A 6 MHz array on a 128-channel system is simulated. The flow setup in the simulation is based on a vessel with a parabolic flow profile for a 60 and 90-degree flow angle. The experimental results are based on the backscattered signal from a sponge mounted in a stepping device. The bias and std. Dev. Of the velocity estimate are calculated for four different flow angles (50,60,75 and 90 degrees). The velocity vector is calculated using the improved 2D estimation approach at a range of depths.

  10. Towards a Social Networks Model for Online Learning & Performance

    ERIC Educational Resources Information Center

    Chung, Kon Shing Kenneth; Paredes, Walter Christian

    2015-01-01

    In this study, we develop a theoretical model to investigate the association between social network properties, "content richness" (CR) in academic learning discourse, and performance. CR is the extent to which one contributes content that is meaningful, insightful and constructive to aid learning and by social network properties we…

  11. Wireless imaging sensor network design and performance analysis

    NASA Astrophysics Data System (ADS)

    Sundaram, Ramakrishnan

    2016-05-01

    This paper discusses (a) the design and implementation of the integrated radio tomographic imaging (RTI) interface for radio signal strength (RSS) data obtained from a wireless imaging sensor network (WISN) (b) the use of model-driven methods to determine the extent of regularization to be applied to reconstruct images from the RSS data, and (c) preliminary study of the performance of the network.

  12. CyNetSVM: A Cytoscape App for Cancer Biomarker Identification Using Network Constrained Support Vector Machines.

    PubMed

    Shi, Xu; Banerjee, Sharmi; Chen, Li; Hilakivi-Clarke, Leena; Clarke, Robert; Xuan, Jianhua

    2017-01-01

    One of the important tasks in cancer research is to identify biomarkers and build classification models for clinical outcome prediction. In this paper, we develop a CyNetSVM software package, implemented in Java and integrated with Cytoscape as an app, to identify network biomarkers using network-constrained support vector machines (NetSVM). The Cytoscape app of NetSVM is specifically designed to improve the usability of NetSVM with the following enhancements: (1) user-friendly graphical user interface (GUI), (2) computationally efficient core program and (3) convenient network visualization capability. The CyNetSVM app has been used to analyze breast cancer data to identify network genes associated with breast cancer recurrence. The biological function of these network genes is enriched in signaling pathways associated with breast cancer progression, showing the effectiveness of CyNetSVM for cancer biomarker identification. The CyNetSVM package is available at Cytoscape App Store and http://sourceforge.net/projects/netsvmjava; a sample data set is also provided at sourceforge.net.

  13. CyNetSVM: A Cytoscape App for Cancer Biomarker Identification Using Network Constrained Support Vector Machines

    PubMed Central

    Chen, Li; Hilakivi-Clarke, Leena; Clarke, Robert

    2017-01-01

    One of the important tasks in cancer research is to identify biomarkers and build classification models for clinical outcome prediction. In this paper, we develop a CyNetSVM software package, implemented in Java and integrated with Cytoscape as an app, to identify network biomarkers using network-constrained support vector machines (NetSVM). The Cytoscape app of NetSVM is specifically designed to improve the usability of NetSVM with the following enhancements: (1) user-friendly graphical user interface (GUI), (2) computationally efficient core program and (3) convenient network visualization capability. The CyNetSVM app has been used to analyze breast cancer data to identify network genes associated with breast cancer recurrence. The biological function of these network genes is enriched in signaling pathways associated with breast cancer progression, showing the effectiveness of CyNetSVM for cancer biomarker identification. The CyNetSVM package is available at Cytoscape App Store and http://sourceforge.net/projects/netsvmjava; a sample data set is also provided at sourceforge.net. PMID:28122019

  14. High-performance, bare silver nanowire network transparent heaters

    NASA Astrophysics Data System (ADS)

    Ergun, Orcun; Coskun, Sahin; Yusufoglu, Yusuf; Emrah Unalan, Husnu

    2016-11-01

    Silver nanowire (Ag NW) networks are one of the most promising candidates for the replacement of indium tin oxide (ITO) thin films in many different applications. Recently, Ag-NW-based transparent heaters (THs) showed excellent heating performance. In order to overcome the instability issues of Ag NW networks, researchers have offered different hybrid structures. However, these approaches not only require extra processing, but also decrease the optical performance of Ag NW networks. So, it is important to investigate and determine the thermal performance limits of bare-Ag-NW-network-based THs. Herein, we report on the effect of NW density, contact geometry, applied bias, flexing and incremental bias application on the TH performance of Ag NW networks. Ag-NW-network-based THs with a sheet resistance and percentage transmittance of 4.3 Ω sq-1 and 83.3%, respectively, and a NW density of 1.6 NW μm-2 reached a maximum temperature of 275 °C under incremental bias application (5 V maximum). With this performance, our results provide a different perspective on bare-Ag-NW-network-based transparent heaters.

  15. High-performance, bare silver nanowire network transparent heaters.

    PubMed

    Ergun, Orcun; Coskun, Sahin; Yusufoglu, Yusuf; Unalan, Husnu Emrah

    2016-11-04

    Silver nanowire (Ag NW) networks are one of the most promising candidates for the replacement of indium tin oxide (ITO) thin films in many different applications. Recently, Ag-NW-based transparent heaters (THs) showed excellent heating performance. In order to overcome the instability issues of Ag NW networks, researchers have offered different hybrid structures. However, these approaches not only require extra processing, but also decrease the optical performance of Ag NW networks. So, it is important to investigate and determine the thermal performance limits of bare-Ag-NW-network-based THs. Herein, we report on the effect of NW density, contact geometry, applied bias, flexing and incremental bias application on the TH performance of Ag NW networks. Ag-NW-network-based THs with a sheet resistance and percentage transmittance of 4.3 Ω sq(-1) and 83.3%, respectively, and a NW density of 1.6 NW μm(-2) reached a maximum temperature of 275 °C under incremental bias application (5 V maximum). With this performance, our results provide a different perspective on bare-Ag-NW-network-based transparent heaters.

  16. IBM SP high-performance networking with a GRF.

    SciTech Connect

    Navarro, J.P.

    1999-05-27

    Increasing use of highly distributed applications, demand for faster data exchange, and highly parallel applications can push the limits of conventional external networking for IBM SP sites. In technical computing applications we have observed a growing use of a pipeline of hosts and networks collaborating to collect, process, and visualize large amounts of realtime data. The GRF, a high-performance IP switch from Ascend and IBM, is the first backbone network switch to offer a media card that can directly connect to an SP Switch. This enables switch attached hosts in an SP complex to communicate at near SP Switch speeds with other GRF attached hosts and networks.

  17. The performance analysis of linux networking - packet receiving

    SciTech Connect

    Wu, Wenji; Crawford, Matt; Bowden, Mark; /Fermilab

    2006-11-01

    The computing models for High-Energy Physics experiments are becoming ever more globally distributed and grid-based, both for technical reasons (e.g., to place computational and data resources near each other and the demand) and for strategic reasons (e.g., to leverage equipment investments). To support such computing models, the network and end systems, computing and storage, face unprecedented challenges. One of the biggest challenges is to transfer scientific data sets--now in the multi-petabyte (10{sup 15} bytes) range and expected to grow to exabytes within a decade--reliably and efficiently among facilities and computation centers scattered around the world. Both the network and end systems should be able to provide the capabilities to support high bandwidth, sustained, end-to-end data transmission. Recent trends in technology are showing that although the raw transmission speeds used in networks are increasing rapidly, the rate of advancement of microprocessor technology has slowed down. Therefore, network protocol-processing overheads have risen sharply in comparison with the time spent in packet transmission, resulting in degraded throughput for networked applications. More and more, it is the network end system, instead of the network, that is responsible for degraded performance of network applications. In this paper, the Linux system's packet receive process is studied from NIC to application. We develop a mathematical model to characterize the Linux packet receiving process. Key factors that affect Linux systems network performance are analyzed.

  18. Differentiation of several interstitial lung disease patterns in HRCT images using support vector machine: role of databases on performance

    NASA Astrophysics Data System (ADS)

    Kale, Mandar; Mukhopadhyay, Sudipta; Dash, Jatindra K.; Garg, Mandeep; Khandelwal, Niranjan

    2016-03-01

    Interstitial lung disease (ILD) is complicated group of pulmonary disorders. High Resolution Computed Tomography (HRCT) considered to be best imaging technique for analysis of different pulmonary disorders. HRCT findings can be categorised in several patterns viz. Consolidation, Emphysema, Ground Glass Opacity, Nodular, Normal etc. based on their texture like appearance. Clinician often find it difficult to diagnosis these pattern because of their complex nature. In such scenario computer-aided diagnosis system could help clinician to identify patterns. Several approaches had been proposed for classification of ILD patterns. This includes computation of textural feature and training /testing of classifier such as artificial neural network (ANN), support vector machine (SVM) etc. In this paper, wavelet features are calculated from two different ILD database, publically available MedGIFT ILD database and private ILD database, followed by performance evaluation of ANN and SVM classifiers in terms of average accuracy. It is found that average classification accuracy by SVM is greater than ANN where trained and tested on same database. Investigation continued further to test variation in accuracy of classifier when training and testing is performed with alternate database and training and testing of classifier with database formed by merging samples from same class from two individual databases. The average classification accuracy drops when two independent databases used for training and testing respectively. There is significant improvement in average accuracy when classifiers are trained and tested with merged database. It infers dependency of classification accuracy on training data. It is observed that SVM outperforms ANN when same database is used for training and testing.

  19. Leveraging Structure to Improve Classification Performance in Sparsely Labeled Networks

    SciTech Connect

    Gallagher, B; Eliassi-Rad, T

    2007-10-22

    We address the problem of classification in a partially labeled network (a.k.a. within-network classification), with an emphasis on tasks in which we have very few labeled instances to start with. Recent work has demonstrated the utility of collective classification (i.e., simultaneous inferences over class labels of related instances) in this general problem setting. However, the performance of collective classification algorithms can be adversely affected by the sparseness of labels in real-world networks. We show that on several real-world data sets, collective classification appears to offer little advantage in general and hurts performance in the worst cases. In this paper, we explore a complimentary approach to within-network classification that takes advantage of network structure. Our approach is motivated by the observation that real-world networks often provide a great deal more structural information than attribute information (e.g., class labels). Through experiments on supervised and semi-supervised classifiers of network data, we demonstrate that a small number of structural features can lead to consistent and sometimes dramatic improvements in classification performance. We also examine the relative utility of individual structural features and show that, in many cases, it is a combination of both local and global network structure that is most informative.

  20. Performance analysis of local area networks

    NASA Technical Reports Server (NTRS)

    Alkhatib, Hasan S.; Hall, Mary Grace

    1990-01-01

    A simulation of the TCP/IP protocol running on a CSMA/CD data link layer was described. The simulation was implemented using the simula language, and object oriented discrete event language. It allows the user to set the number of stations at run time, as well as some station parameters. Those parameters are the interrupt time and the dma transfer rate for each station. In addition, the user may configure the network at run time with stations of differing characteristics. Two types are available, and the parameters of both types are read from input files at run time. The parameters include the dma transfer rate, interrupt time, data rate, average message size, maximum frame size and the average interarrival time of messages per station. The information collected for the network is the throughput and the mean delay per packet. For each station, the number of messages attempted as well as the number of messages successfully transmitted is collected in addition to the throughput and mean packet delay per station.

  1. Optical interconnection networks for high-performance computing systems.

    PubMed

    Biberman, Aleksandr; Bergman, Keren

    2012-04-01

    Enabled by silicon photonic technology, optical interconnection networks have the potential to be a key disruptive technology in computing and communication industries. The enduring pursuit of performance gains in computing, combined with stringent power constraints, has fostered the ever-growing computational parallelism associated with chip multiprocessors, memory systems, high-performance computing systems and data centers. Sustaining these parallelism growths introduces unique challenges for on- and off-chip communications, shifting the focus toward novel and fundamentally different communication approaches. Chip-scale photonic interconnection networks, enabled by high-performance silicon photonic devices, offer unprecedented bandwidth scalability with reduced power consumption. We demonstrate that the silicon photonic platforms have already produced all the high-performance photonic devices required to realize these types of networks. Through extensive empirical characterization in much of our work, we demonstrate such feasibility of waveguides, modulators, switches and photodetectors. We also demonstrate systems that simultaneously combine many functionalities to achieve more complex building blocks. We propose novel silicon photonic devices, subsystems, network topologies and architectures to enable unprecedented performance of these photonic interconnection networks. Furthermore, the advantages of photonic interconnection networks extend far beyond the chip, offering advanced communication environments for memory systems, high-performance computing systems, and data centers.

  2. Challenges for high-performance networking for exascale computing.

    SciTech Connect

    Barrett, Brian W.; Hemmert, K. Scott; Underwood, Keith Douglas; Brightwell, Ronald Brian

    2010-05-01

    Achieving the next three orders of magnitude performance increase to move from petascale to exascale computing will require a significant advancements in several fundamental areas. Recent studies have outlined many of the challenges in hardware and software that will be needed. In this paper, we examine these challenges with respect to high-performance networking. We describe the repercussions of anticipated changes to computing and networking hardware and discuss the impact that alternative parallel programming models will have on the network software stack. We also present some ideas on possible approaches that address some of these challenges.

  3. Success factors in hospital network performance: evidence from Korea.

    PubMed

    Kim, Kwang-Jum; Burns, Lawton R

    2007-08-01

    Collaborative networks have become a common organizational strategy to deal with uncertain and dynamic environments. Like their counterparts in the USA, Korean hospitals are establishing cooperative relationships with one another, with varying performance results. This paper analyses some of the sources of variation in hospital network performance and identifies some of the possible success factors. The study finds that the quality of cooperation and information sharing between network partners are critical. The paper concludes with a discussion of the implications for researchers and practitioners.

  4. Arrhythmia Identification with Two-Lead Electrocardiograms Using Artificial Neural Networks and Support Vector Machines for a Portable ECG Monitor System

    PubMed Central

    Liu, Shing-Hong; Cheng, Da-Chuan; Lin, Chih-Ming

    2013-01-01

    An automatic configuration that can detect the position of R-waves, classify the normal sinus rhythm (NSR) and other four arrhythmic types from the continuous ECG signals obtained from the MIT-BIH arrhythmia database is proposed. In this configuration, a support vector machine (SVM) was used to detect and mark the ECG heartbeats with raw signals and differential signals of a lead ECG. An algorithm based on the extracted markers segments waveforms of Lead II and V1 of the ECG as the pattern classification features. A self-constructing neural fuzzy inference network (SoNFIN) was used to classify NSR and four arrhythmia types, including premature ventricular contraction (PVC), premature atrium contraction (PAC), left bundle branch block (LBBB), and right bundle branch block (RBBB). In a real scenario, the classification results show the accuracy achieved is 96.4%. This performance is suitable for a portable ECG monitor system for home care purposes. PMID:23303379

  5. 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.

  6. Performance Analysis of a NASA Integrated Network Array

    NASA Technical Reports Server (NTRS)

    Nessel, James A.

    2012-01-01

    The Space Communications and Navigation (SCaN) Program is planning to integrate its individual networks into a unified network which will function as a single entity to provide services to user missions. This integrated network architecture is expected to provide SCaN customers with the capabilities to seamlessly use any of the available SCaN assets to support their missions to efficiently meet the collective needs of Agency missions. One potential optimal application of these assets, based on this envisioned architecture, is that of arraying across existing networks to significantly enhance data rates and/or link availabilities. As such, this document provides an analysis of the transmit and receive performance of a proposed SCaN inter-network antenna array. From the study, it is determined that a fully integrated internetwork array does not provide any significant advantage over an intra-network array, one in which the assets of an individual network are arrayed for enhanced performance. Therefore, it is the recommendation of this study that NASA proceed with an arraying concept, with a fundamental focus on a network-centric arraying.

  7. High-Performance Satellite/Terrestrial-Network Gateway

    NASA Technical Reports Server (NTRS)

    Beering, David R.

    2005-01-01

    A gateway has been developed to enable digital communication between (1) the high-rate receiving equipment at NASA's White Sands complex and (2) a standard terrestrial digital communication network at data rates up to 622 Mb/s. The design of this gateway can also be adapted for use in commercial Earth/satellite and digital communication networks, and in terrestrial digital communication networks that include wireless subnetworks. Gateway as used here signifies an electronic circuit that serves as an interface between two electronic communication networks so that a computer (or other terminal) on one network can communicate with a terminal on the other network. The connection between this gateway and the high-rate receiving equipment is made via a synchronous serial data interface at the emitter-coupled-logic (ECL) level. The connection between this gateway and a standard asynchronous transfer mode (ATM) terrestrial communication network is made via a standard user network interface with a synchronous optical network (SONET) connector. The gateway contains circuitry that performs the conversion between the ECL and SONET interfaces. The data rate of the SONET interface can be either 155.52 or 622.08 Mb/s. The gateway derives its clock signal from a satellite modem in the high-rate receiving equipment and, hence, is agile in the sense that it adapts to the data rate of the serial interface.

  8. Urban traffic-network performance: flow theory and simulation experiments

    SciTech Connect

    Williams, J.C.

    1986-01-01

    Performance models for urban street networks were developed to describe the response of a traffic network to given travel-demand levels. The three basic traffic flow variables, speed, flow, and concentration, are defined at the network level, and three model systems are proposed. Each system consists of a series of interrelated, consistent functions between the three basic traffic-flow variables as well as the fraction of stopped vehicles in the network. These models are subsequently compared with the results of microscopic simulation of a small test network. The sensitivity of one of the model systems to a variety of network features was also explored. Three categories of features were considered, with the specific features tested listed in parentheses: network topology (block length and street width), traffic control (traffic signal coordination), and traffic characteristics (level of inter-vehicular interaction). Finally, a fundamental issue concerning the estimation of two network-level parameters (from a nonlinear relation in the two-fluid theory) was examined. The principal concern was that of comparability of these parameters when estimated with information from a single vehicle (or small group of vehicles), as done in conjunction with previous field studies, and when estimated with network-level information (i.e., all the vehicles), as is possible with simulation.

  9. Performance characteristics of a variable-area vane nozzle for vectoring an ASTOVL exhaust jet up to 45 deg

    NASA Technical Reports Server (NTRS)

    Mcardle, Jack G.; Esker, Barbara S.

    1993-01-01

    Many conceptual designs for advanced short-takeoff, vertical landing (ASTOVL) aircraft need exhaust nozzles that can vector the jet to provide forces and moments for controlling the aircraft's movement or attitude in flight near the ground. A type of nozzle that can both vector the jet and vary the jet flow area is called a vane nozzle. Basically, the nozzle consists of parallel, spaced-apart flow passages formed by pairs of vanes (vanesets) that can be rotated on axes perpendicular to the flow. Two important features of this type of nozzle are the abilities to vector the jet rearward up to 45 degrees and to produce less harsh pressure and velocity footprints during vertical landing than does an equivalent single jet. A one-third-scale model of a generic vane nozzle was tested with unheated air at the NASA Lewis Research Center's Powered Lift Facility. The model had three parallel flow passages. Each passage was formed by a vaneset consisting of a long and a short vane. The longer vanes controlled the jet vector angle, and the shorter controlled the flow area. Nozzle performance for three nominal flow areas (basic and plus or minus 21 percent of basic area), each at nominal jet vector angles from -20 deg (forward of vertical) to +45 deg (rearward of vertical) are presented. The tests were made with the nozzle mounted on a model tailpipe with a blind flange on the end to simulate a closed cruise nozzle, at tailpipe-to-ambient pressure ratios from 1.8 to 4.0. Also included are jet wake data, single-vaneset vector performance for long/short and equal-length vane designs, and pumping capability. The pumping capability arises from the subambient pressure developed in the cavities between the vanesets, which could be used to aspirate flow from a source such as the engine compartment. Some of the performance characteristics are compared with characteristics of a single-jet nozzle previously reported.

  10. Body Area Networks performance analysis using UWB.

    PubMed

    Fatehy, Mohammed; Kohno, Ryuji

    2013-01-01

    The successful realization of a Wireless Body Area Network (WBAN) using Ultra Wideband (UWB) technology supports different medical and consumer electronics (CE) applications but stand in a need for an innovative solution to meet the different requirements of these applications. Previously, we proposed to use adaptive processing gain (PG) to fulfill the different QoS requirements of these WBAN applications. In this paper, interference occurred between two different BANs in a UWB-based system has been analyzed in terms of acceptable ratio of overlapping between these BANs' PG providing the required QoS for each BAN. The first BAN employed for a healthcare device (e.g. EEG, ECG, etc.) with a relatively longer spreading sequence is used and the second customized for entertainment application (e.g. wireless headset, wireless game pad, etc.) where a shorter spreading code is assigned. Considering bandwidth utilization and difference in the employed spreading sequence, the acceptable ratio of overlapping between these BANs should fall between 0.05 and 0.5 in order to optimize the used spreading sequence and in the meantime satisfying the required QoS for these applications.

  11. Static internal performance of single-expansion-ramp nozzles with thrust-vectoring capability up to 60 deg

    NASA Technical Reports Server (NTRS)

    Berrier, B. L.; Leavitt, L. D.

    1984-01-01

    An investigation has been conducted at static conditions (wind off) in the static-test facility of the Langley 16-Foot Transonic Tunnel. The effects of geometric thrust-vector angle, sidewall containment, ramp curvature, lower-flap lip angle, and ramp length on the internal performance of nonaxisymmetric single-expansion-ramp nozzles were investigated. Geometric thrust-vector angle was varied from -20 deg. to 60 deg., and nozzle pressure ratio was varied from 1.0 (jet off) to approximately 10.0.

  12. A Comprehensive Performance Comparison of On-Demand Routing Protocols in Mobile Ad-Hoc Networks

    NASA Astrophysics Data System (ADS)

    Khan, Jahangir; Hayder, Syed Irfan

    Mobile ad hoc network is an autonomous system of mobile nodes connected by wireless links. Each node operates not only as an end system, but also as a router to forward packets. The nodes are free to move about and organize themselves on a fly. In this paper we focus on the performance of the on-demand routing protocols such as DSR and AODV in ad-hoc networks. We have observed the performance change of each protocol through simulation with varying the data in intermediate nodes and to compare data throughput in each mobile modes of each protocol to analyze the packet fraction for application data. The objective of this work is to evaluate two routing protocols such as On-demand behavior, namely, Ad hoc Demand Distance vector (AODV) and Dynamic Source Routing (DSR), for wireless ad hoc networks based on performance of intermediate nodes for the delivery of data form source to destination and vice versa in order to compare the efficiency of throughput in the neighbors nodes. To overcome we have proposed OPNET simulator for performance comparison of hop to hop delivery of data packet in autonomous system.

  13. Diversity improves performance in excitable networks

    PubMed Central

    Copelli, Mauro; Roberts, James A.

    2016-01-01

    As few real systems comprise indistinguishable units, diversity is a hallmark of nature. Diversity among interacting units shapes properties of collective behavior such as synchronization and information transmission. However, the benefits of diversity on information processing at the edge of a phase transition, ordinarily assumed to emerge from identical elements, remain largely unexplored. Analyzing a general model of excitable systems with heterogeneous excitability, we find that diversity can greatly enhance optimal performance (by two orders of magnitude) when distinguishing incoming inputs. Heterogeneous systems possess a subset of specialized elements whose capability greatly exceeds that of the nonspecialized elements. We also find that diversity can yield multiple percolation, with performance optimized at tricriticality. Our results are robust in specific and more realistic neuronal systems comprising a combination of excitatory and inhibitory units, and indicate that diversity-induced amplification can be harnessed by neuronal systems for evaluating stimulus intensities. PMID:27168961

  14. Performance limitations for networked control systems with plant uncertainty

    NASA Astrophysics Data System (ADS)

    Chi, Ming; Guan, Zhi-Hong; Cheng, Xin-Ming; Yuan, Fu-Shun

    2016-04-01

    There has recently been significant interest in performance study for networked control systems with communication constraints. But the existing work mainly assumes that the plant has an exact model. The goal of this paper is to investigate the optimal tracking performance for networked control system in the presence of plant uncertainty. The plant under consideration is assumed to be non-minimum phase and unstable, while the two-parameter controller is employed and the integral square criterion is adopted to measure the tracking error. And we formulate the uncertainty by utilising stochastic embedding. The explicit expression of the tracking performance has been obtained. The results show that the network communication noise and the model uncertainty, as well as the unstable poles and non-minimum phase zeros, can worsen the tracking performance.

  15. Project Performance Evaluation Using Deep Belief Networks

    NASA Astrophysics Data System (ADS)

    Nguvulu, Alick; Yamato, Shoso; Honma, Toshihisa

    A Project Assessment Indicator (PAI) Model has recently been applied to evaluate monthly project performance based on 15 project elements derived from the project management (PM) knowledge areas. While the PAI Model comprehensively evaluates project performance, it lacks objectivity and universality. It lacks objectivity because experts assign model weights intuitively based on their PM skills and experience. It lacks universality because the allocation of ceiling scores to project elements is done ad hoc based on the empirical rule without taking into account the interactions between the project elements. This study overcomes these limitations by applying a DBN approach where the model automatically assigns weights and allocates ceiling scores to the project elements based on the DBN weights which capture the interaction between the project elements. We train our DBN on 5 IT projects of 12 months duration and test it on 8 IT projects with less than 12 months duration. We completely eliminate the manual assigning of weights and compute ceiling scores of project elements based on DBN weights. Our trained DBN evaluates monthly project performance of the 8 test projects based on the 15 project elements to within a monthly relative error margin of between ±1.03 and ±3.30%.

  16. Performance analysis of a common-mode signal based low-complexity crosstalk cancelation scheme in vectored VDSL

    NASA Astrophysics Data System (ADS)

    Zafaruddin, SM; Prakriya, Shankar; Prasad, Surendra

    2012-12-01

    In this article, we propose a vectored system by using both common mode (CM) and differential mode (DM) signals in upstream VDSL. We first develop a multi-input multi-output (MIMO) CM channel by using the single-pair CM and MIMO DM channels proposed recently, and study the characteristics of the resultant CM-DM channel matrix. We then propose a low complexity receiver structure in which the CM and DM signals of each twisted-pair (TP) are combined before the application of a MIMO zero forcing (ZF) receiver. We study capacity of the proposed system, and show that the vectored CM-DM processing provides higher data-rates at longer loop-lengths. In the absence of alien crosstalk, application of the ZF receiver on the vectored CM-DM signals yields performance close to the single user bound (SUB). In the presence of alien crosstalk, we show that the vectored CM-DM processing exploits the spatial correlation of CM and DM signals and provides higher data rates than with DM processing only. Simulation results validate the analysis and demonstrate the importance of CM-DM joint processing in vectored VDSL systems.

  17. Virulence Factors of Geminivirus Interact with MYC2 to Subvert Plant Resistance and Promote Vector Performance[C][W

    PubMed Central

    Li, Ran; Weldegergis, Berhane T.; Li, Jie; Jung, Choonkyun; Qu, Jing; Sun, Yanwei; Qian, Hongmei; Tee, ChuanSia; van Loon, Joop J.A.; Dicke, Marcel; Chua, Nam-Hai; Liu, Shu-Sheng

    2014-01-01

    A pathogen may cause infected plants to promote the performance of its transmitting vector, which accelerates the spread of the pathogen. This positive effect of a pathogen on its vector via their shared host plant is termed indirect mutualism. For example, terpene biosynthesis is suppressed in begomovirus-infected plants, leading to reduced plant resistance and enhanced performance of the whiteflies (Bemisia tabaci) that transmit these viruses. Although begomovirus-whitefly mutualism has been known, the underlying mechanism is still elusive. Here, we identified βC1 of Tomato yellow leaf curl China virus, a monopartite begomovirus, as the viral genetic factor that suppresses plant terpene biosynthesis. βC1 directly interacts with the basic helix-loop-helix transcription factor MYC2 to compromise the activation of MYC2-regulated terpene synthase genes, thereby reducing whitefly resistance. MYC2 associates with the bipartite begomoviral protein BV1, suggesting that MYC2 is an evolutionarily conserved target of begomoviruses for the suppression of terpene-based resistance and the promotion of vector performance. Our findings describe how this viral pathogen regulates host plant metabolism to establish mutualism with its insect vector. PMID:25490915

  18. High-performance network and channel based storage

    NASA Technical Reports Server (NTRS)

    Katz, Randy H.

    1992-01-01

    In the traditional mainframe-centered view of a computer system, storage devices are coupled to the system through complex hardware subsystems called I/O channels. With the dramatic shift toward workstation-based computing, and its associated client/server model of computation, storage facilities are now found attached to file servers and distributed throughout the network. In this paper, we discuss the underlying technology trends that are leading to high-performance network-based storage, namely advances in networks, storage devices, and I/O controller and server architectures. We review several commercial systems and research prototypes that are leading to a new approach to high-performance computing based on network-attached storage.

  19. High-performance multicasting schemes in optical packet switched networks

    NASA Astrophysics Data System (ADS)

    Ji, Yuefeng; Liu, Xin; Zhang, Jie; Zhang, Min

    2009-11-01

    Current trends in communications indicate that multicasting is becoming increasingly popular and important in networking applications. Since multicasting can be supported more efficiently in optical domain by utilizing the inherent light-splitting capacity of optical devices, such as optical splitters, than by copying data in electronic domain, issues concerning running multicast sessions in the all-optical networks have received much attention in recent years. In this paper, different multicasting schemes and their performance in the Optical Packet Switched networks are investigated, including the parallel mode, serial mode, and hybrid mode multicasting schemes. Computer simulation results show that compared with the parallel-mode and serial-mode multicasting schemes, hybrid-mode multicasting scheme is the best way to deliver multicast sessions in the Optical Packet Switched networks due to its highest performance.

  20. High performance network and channel-based storage

    NASA Technical Reports Server (NTRS)

    Katz, Randy H.

    1991-01-01

    In the traditional mainframe-centered view of a computer system, storage devices are coupled to the system through complex hardware subsystems called input/output (I/O) channels. With the dramatic shift towards workstation-based computing, and its associated client/server model of computation, storage facilities are now found attached to file servers and distributed throughout the network. We discuss the underlying technology trends that are leading to high performance network-based storage, namely advances in networks, storage devices, and I/O controller and server architectures. We review several commercial systems and research prototypes that are leading to a new approach to high performance computing based on network-attached storage.

  1. The Use of Neural Network Technology to Model Swimming Performance

    PubMed Central

    Silva, António José; Costa, Aldo Manuel; Oliveira, Paulo Moura; Reis, Victor Machado; Saavedra, José; Perl, Jurgen; Rouboa, Abel; Marinho, Daniel Almeida

    2007-01-01

    The aims of the present study were: to identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons) and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females) of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility), swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics) and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron) with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances) is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports. Key pointsThe non-linear analysis resulting from the use of feed forward neural network allowed us the development of four performance models.The mean difference between the true and estimated results performed by each one of the four neural network models constructed was low.The neural network tool can be a good approach in the resolution of the performance modeling as an alternative to the standard statistical models that presume well-defined distributions and independence among all inputs.The use of neural networks for sports

  2. 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.

  3. Bearing performance degradation assessment based on time-frequency code features and SOM network

    NASA Astrophysics Data System (ADS)

    Zhang, Yan; Tang, Baoping; Han, Yan; Deng, Lei

    2017-04-01

    Bearing performance degradation assessment and prognostics are extremely important in supporting maintenance decision and guaranteeing the system’s reliability. To achieve this goal, this paper proposes a novel feature extraction method for the degradation assessment and prognostics of bearings. Features of time-frequency codes (TFCs) are extracted from the time-frequency distribution using a hybrid procedure based on short-time Fourier transform (STFT) and non-negative matrix factorization (NMF) theory. An alternative way to design the health indicator is investigated by quantifying the similarity between feature vectors using a self-organizing map (SOM) network. On the basis of this idea, a new health indicator called time-frequency code quantification error (TFCQE) is proposed to assess the performance degradation of the bearing. This indicator is constructed based on the bearing real-time behavior and the SOM model that is previously trained with only the TFC vectors under the normal condition. Vibration signals collected from the bearing run-to-failure tests are used to validate the developed method. The comparison results demonstrate the superiority of the proposed TFCQE indicator over many other traditional features in terms of feature quality metrics, incipient degradation identification and achieving accurate prediction. Highlights • Time-frequency codes are extracted to reflect the signals’ characteristics. • SOM network served as a tool to quantify the similarity between feature vectors. • A new health indicator is proposed to demonstrate the whole stage of degradation development. • The method is useful for extracting the degradation features and detecting the incipient degradation. • The superiority of the proposed method is verified using experimental data.

  4. Runtime Performance and Virtual Network Control Alternatives in VM-Based High-Fidelity Network Simulations

    DTIC Science & Technology

    2012-12-01

    described in detail in (Yoginath, Perumalla and Henz 2012). The MPI benchmarks comprise two scenarios, namely, Constant Network Delay ( CND ) and...Varying Network Delay (VND). With CND , we evaluate the performance of NSX and CSX scheduler support for time-ordered event execution when the...identifier of the jth message in the ith run. CND Benchmark Performance Figure 2: CND benchmark error plots (left); CND benchmark runtime plots

  5. Hospital network performance: a survey of hospital stakeholders' perspectives.

    PubMed

    Bravi, F; Gibertoni, D; Marcon, A; Sicotte, C; Minvielle, E; Rucci, P; Angelastro, A; Carradori, T; Fantini, M P

    2013-02-01

    Hospital networks are an emerging organizational form designed to face the new challenges of public health systems. Although the benefits introduced by network models in terms of rationalization of resources are known, evidence about stakeholders' perspectives on hospital network performance from the literature is scanty. Using the Competing Values Framework of organizational effectiveness and its subsequent adaptation by Minvielle et al., we conducted in 2009 a survey in five hospitals of an Italian network for oncological care to examine and compare the views on hospital network performance of internal stakeholders (physicians, nurses and the administrative staff). 329 questionnaires exploring stakeholders' perspectives were completed, with a response rate of 65.8%. Using exploratory factor analysis of the 66 items of the questionnaire, we identified 4 factors, i.e. Centrality of relationships, Quality of care, Attractiveness/Reputation and Staff empowerment and Protection of workers' rights. 42 items were retained in the analysis. Factor scores proved to be high (mean score>8 on a 10-item scale), except for Attractiveness/Reputation (mean score 6.79), indicating that stakeholders attach a higher importance to relational and health care aspects. Comparison of factor scores among stakeholders did not reveal significant differences, suggesting a broadly shared view on hospital network performance.

  6. UltraSciencenet: High- Performance Network Research Test-Bed

    SciTech Connect

    Rao, Nageswara S; Wing, William R; Poole, Stephen W; Hicks, Susan Elaine; DeNap, Frank A; Carter, Steven M; Wu, Qishi

    2009-04-01

    The high-performance networking requirements for next generation large-scale applications belong to two broad classes: (a) high bandwidths, typically multiples of 10Gbps, to support bulk data transfers, and (b) stable bandwidths, typically at much lower bandwidths, to support computational steering, remote visualization, and remote control of instrumentation. Current Internet technologies, however, are severely limited in meeting these demands because such bulk bandwidths are available only in the backbone, and stable control channels are hard to realize over shared connections. The UltraScience Net (USN) facilitates the development of such technologies by providing dynamic, cross-country dedicated 10Gbps channels for large data transfers, and 150 Mbps channels for interactive and control operations. Contributions of the USN project are two-fold: (a) Infrastructure Technologies for Network Experimental Facility: USN developed and/or demonstrated a number of infrastructure technologies needed for a national-scale network experimental facility. Compared to Internet, USN's data-plane is different in that it can be partitioned into isolated layer-1 or layer-2 connections, and its control-plane is different in the ability of users and applications to setup and tear down channels as needed. Its design required several new components including a Virtual Private Network infrastructure, a bandwidth and channel scheduler, and a dynamic signaling daemon. The control-plane employs a centralized scheduler to compute the channel allocations and a signaling daemon to generate configuration signals to switches. In a nutshell, USN demonstrated the ability to build and operate a stable national-scale switched network. (b) Structured Network Research Experiments: A number of network research experiments have been conducted on USN that cannot be easily supported over existing network facilities, including test-beds and production networks. It settled an open matter by demonstrating

  7. Efficient resting-state EEG network facilitates motor imagery performance

    NASA Astrophysics Data System (ADS)

    Zhang, Rui; Yao, Dezhong; Valdés-Sosa, Pedro A.; Li, Fali; Li, Peiyang; Zhang, Tao; Ma, Teng; Li, Yongjie; Xu, Peng

    2015-12-01

    Objective. Motor imagery-based brain-computer interface (MI-BCI) systems hold promise in motor function rehabilitation and assistance for motor function impaired people. But the ability to operate an MI-BCI varies across subjects, which becomes a substantial problem for practical BCI applications beyond the laboratory. Approach. Several previous studies have demonstrated that individual MI-BCI performance is related to the resting state of brain. In this study, we further investigate offline MI-BCI performance variations through the perspective of resting-state electroencephalography (EEG) network. Main results. Spatial topologies and statistical measures of the network have close relationships with MI classification accuracy. Specifically, mean functional connectivity, node degrees, edge strengths, clustering coefficient, local efficiency and global efficiency are positively correlated with MI classification accuracy, whereas the characteristic path length is negatively correlated with MI classification accuracy. The above results indicate that an efficient background EEG network may facilitate MI-BCI performance. Finally, a multiple linear regression model was adopted to predict subjects’ MI classification accuracy based on the efficiency measures of the resting-state EEG network, resulting in a reliable prediction. Significance. This study reveals the network mechanisms of the MI-BCI and may help to find new strategies for improving MI-BCI performance.

  8. Equivalent Vectors

    ERIC Educational Resources Information Center

    Levine, Robert

    2004-01-01

    The cross-product is a mathematical operation that is performed between two 3-dimensional vectors. The result is a vector that is orthogonal or perpendicular to both of them. Learning about this for the first time while taking Calculus-III, the class was taught that if AxB = AxC, it does not necessarily follow that B = C. This seemed baffling. The…

  9. Adaptive Optimization of Aircraft Engine Performance Using Neural Networks

    NASA Technical Reports Server (NTRS)

    Simon, Donald L.; Long, Theresa W.

    1995-01-01

    Preliminary results are presented on the development of an adaptive neural network based control algorithm to enhance aircraft engine performance. This work builds upon a previous National Aeronautics and Space Administration (NASA) effort known as Performance Seeking Control (PSC). PSC is an adaptive control algorithm which contains a model of the aircraft's propulsion system which is updated on-line to match the operation of the aircraft's actual propulsion system. Information from the on-line model is used to adapt the control system during flight to allow optimal operation of the aircraft's propulsion system (inlet, engine, and nozzle) to improve aircraft engine performance without compromising reliability or operability. Performance Seeking Control has been shown to yield reductions in fuel flow, increases in thrust, and reductions in engine fan turbine inlet temperature. The neural network based adaptive control, like PSC, will contain a model of the propulsion system which will be used to calculate optimal control commands on-line. Hopes are that it will be able to provide some additional benefits above and beyond those of PSC. The PSC algorithm is computationally intensive, it is valid only at near steady-state flight conditions, and it has no way to adapt or learn on-line. These issues are being addressed in the development of the optimal neural controller. Specialized neural network processing hardware is being developed to run the software, the algorithm will be valid at steady-state and transient conditions, and will take advantage of the on-line learning capability of neural networks. Future plans include testing the neural network software and hardware prototype against an aircraft engine simulation. In this paper, the proposed neural network software and hardware is described and preliminary neural network training results are presented.

  10. Sub-terahertz spectroscopy of magnetic resonance in BiFeO3 using a vector network analyzer

    NASA Astrophysics Data System (ADS)

    Caspers, Christian; Gandhi, Varun P.; Magrez, Arnaud; de Rijk, Emile; Ansermet, Jean-Philippe

    2016-06-01

    Detection of sub-THz spin cycloid resonances (SCRs) of stoichiometric BiFeO3 (BFO) was demonstrated using a vector network analyzer. Continuous wave absorption spectroscopy is possible, thanks to heterodyning and electronic sweep control using frequency extenders for frequencies from 480 to 760 GHz. High frequency resolution reveals SCR absorption peaks with a frequency precision in the ppm regime. Three distinct SCR features of BFO were observed and identified as Ψ1 and Φ2 modes, which are out-of-plane and in-plane modes of the spin cycloid, respectively. A spin reorientation transition at 200 K is evident in the frequency vs temperature study. The global minimum in linewidth for both Ψ modes at 140 K is ascribed to the critical slowing down of spin fluctuations.

  11. Input vector optimization of feed-forward neural networks for fitting ab initio potential-energy databases

    NASA Astrophysics Data System (ADS)

    Malshe, M.; Raff, L. M.; Hagan, M.; Bukkapatnam, S.; Komanduri, R.

    2010-05-01

    The variation in the fitting accuracy of neural networks (NNs) when used to fit databases comprising potential energies obtained from ab initio electronic structure calculations is investigated as a function of the number and nature of the elements employed in the input vector to the NN. Ab initio databases for H2O2, HONO, Si5, and H2CCHBr were employed in the investigations. These systems were chosen so as to include four-, five-, and six-body systems containing first, second, third, and fourth row elements with a wide variety of chemical bonding and whose conformations cover a wide range of structures that occur under high-energy machining conditions and in chemical reactions involving cis-trans isomerizations, six different types of two-center bond ruptures, and two different three-center dissociation reactions. The ab initio databases for these systems were obtained using density functional theory/B3LYP, MP2, and MP4 methods with extended basis sets. A total of 31 input vectors were investigated. In each case, the elements of the input vector were chosen from interatomic distances, inverse powers of the interatomic distance, three-body angles, and dihedral angles. Both redundant and nonredundant input vectors were investigated. The results show that among all the input vectors investigated, the set employed in the Z-matrix specification of the molecular configurations in the electronic structure calculations gave the lowest NN fitting accuracy for both Si5 and vinyl bromide. The underlying reason for this result appears to be the discontinuity present in the dihedral angle for planar geometries. The use of trigometric functions of the angles as input elements produced significantly improved fitting accuracy as this choice eliminates the discontinuity. The most accurate fitting was obtained when the elements of the input vector were taken to have the form Rij-n, where the Rij are the interatomic distances. When the Levenberg-Marquardt procedure was modified

  12. Performance of Social Network Sensors during Hurricane Sandy

    PubMed Central

    Kryvasheyeu, Yury; Chen, Haohui; Moro, Esteban; Van Hentenryck, Pascal; Cebrian, Manuel

    2015-01-01

    Information flow during catastrophic events is a critical aspect of disaster management. Modern communication platforms, in particular online social networks, provide an opportunity to study such flow and derive early-warning sensors, thus improving emergency preparedness and response. Performance of the social networks sensor method, based on topological and behavioral properties derived from the “friendship paradox”, is studied here for over 50 million Twitter messages posted before, during, and after Hurricane Sandy. We find that differences in users’ network centrality effectively translate into moderate awareness advantage (up to 26 hours); and that geo-location of users within or outside of the hurricane-affected area plays a significant role in determining the scale of such an advantage. Emotional response appears to be universal regardless of the position in the network topology, and displays characteristic, easily detectable patterns, opening a possibility to implement a simple “sentiment sensing” technique that can detect and locate disasters. PMID:25692690

  13. Performance of social network sensors during Hurricane Sandy.

    PubMed

    Kryvasheyeu, Yury; Chen, Haohui; Moro, Esteban; Van Hentenryck, Pascal; Cebrian, Manuel

    2015-01-01

    Information flow during catastrophic events is a critical aspect of disaster management. Modern communication platforms, in particular online social networks, provide an opportunity to study such flow and derive early-warning sensors, thus improving emergency preparedness and response. Performance of the social networks sensor method, based on topological and behavioral properties derived from the "friendship paradox", is studied here for over 50 million Twitter messages posted before, during, and after Hurricane Sandy. We find that differences in users' network centrality effectively translate into moderate awareness advantage (up to 26 hours); and that geo-location of users within or outside of the hurricane-affected area plays a significant role in determining the scale of such an advantage. Emotional response appears to be universal regardless of the position in the network topology, and displays characteristic, easily detectable patterns, opening a possibility to implement a simple "sentiment sensing" technique that can detect and locate disasters.

  14. Investigation into the relationship between the gravity vector and the flow vector to improve performance in two-phase continuous flow biodiesel reactor.

    PubMed

    Unker, S A; Boucher, M B; Hawley, K R; Midgette, A A; Stuart, J D; Parnas, R S

    2010-10-01

    The following study analyzes the performance of a continuous flow biodiesel reactor/separator. The reactor achieves high conversion of vegetable oil triglycerides to biodiesel while simultaneously separating co-product glycerol. The influence of the flow direction, relative to the gravity vector, on the reactor performance was measured. Reactor performance was assessed by both the conversion of vegetable oil triglycerides to biodiesel and the separation efficiency of removing the co-product glycerol. At slightly elevated temperatures of 40-50 degrees C, an overall feed of 1.2 L/min, a 6:1 M ratio of methanol to vegetable oil triglycerides, and a 1-1.3 wt.% potassium hydroxide catalyst loading, the reactor converted more than 96% of the pretreated waste vegetable oil to biodiesel. The reactor also separated 36-95% of the glycerol that was produced. Tilting the reactor away from the vertical direction produced a large increase in glycerol separation efficiency and only a small decrease in conversion.

  15. On a vector space representation in genetic algorithms for sensor scheduling in wireless sensor networks.

    PubMed

    Martins, F V C; Carrano, E G; Wanner, E F; Takahashi, R H C; Mateus, G R; Nakamura, F G

    2014-01-01

    Recent works raised the hypothesis that the assignment of a geometry to the decision variable space of a combinatorial problem could be useful both for providing meaningful descriptions of the fitness landscape and for supporting the systematic construction of evolutionary operators (the geometric operators) that make a consistent usage of the space geometric properties in the search for problem optima. This paper introduces some new geometric operators that constitute the realization of searches along the combinatorial space versions of the geometric entities descent directions and subspaces. The new geometric operators are stated in the specific context of the wireless sensor network dynamic coverage and connectivity problem (WSN-DCCP). A genetic algorithm (GA) is developed for the WSN-DCCP using the proposed operators, being compared with a formulation based on integer linear programming (ILP) which is solved with exact methods. That ILP formulation adopts a proxy objective function based on the minimization of energy consumption in the network, in order to approximate the objective of network lifetime maximization, and a greedy approach for dealing with the system's dynamics. To the authors' knowledge, the proposed GA is the first algorithm to outperform the lifetime of networks as synthesized by the ILP formulation, also running in much smaller computational times for large instances.

  16. Effect of traffic self-similarity on network performance

    NASA Astrophysics Data System (ADS)

    Park, Kihong; Kim, Gitae; Crovella, Mark E.

    1997-10-01

    Recent measurements of network traffic have shown that self- similarity is an ubiquitous phenomenon present in both local area and wide area traffic traces. In previous work, we have shown a simple, robust application layer causal mechanism of traffic self-similarity, namely, the transfer of files in a network system where the file size distributions are heavy- tailed. In this paper, we study the effect of scale- invariant burstiness on network performance when the functionality of the transport layer and the interaction of traffic sources sharing bounded network resources is incorporated. First, we show that transport layer mechanisms are important factors in translating the application layer causality into link traffic self-similarity. Network performance as captured by throughput, packet loss rate, and packet retransmission rate degrades gradually with increased heavy-tailedness while queueing delay, response time, and fairness deteriorate more drastically. The degree to which heavy-tailedness affects self-similarity is determined by how well congestion control is able to shape a source traffic into an on-average constant output stream while conserving information. Second, we show that increasing network resources such as link bandwidth and buffer capacity results in a superlinear improvement in performance. When large file transfers occur with nonnegligible probability, the incremental improvement in throughput achieved for large buffer sizes is accompanied by long queueing delays vis-a- vis the case when the file size distribution is not heavy- tailed. Buffer utilization continues to remain at a high level implying that further improvement in throughput is only achieved at the expense of a disproportionate increase in queueing delay. A similar trade-off relationship exists between queueing delay and packet loss rate, the curvature of the performance curve being highly sensitive to the degree of self-similarity. Third, we investigate the effect of congestion

  17. Integrated healthcare networks' performance: a growth curve modeling approach.

    PubMed

    Wan, Thomas T H; Wang, Bill B L

    2003-05-01

    This study examines the effects of integration on the performance ratings of the top 100 integrated healthcare networks (IHNs) in the United States. A strategic-contingency theory is used to identify the relationship of IHNs' performance to their structural and operational characteristics and integration strategies. To create a database for the panel study, the top 100 IHNs selected by the SMG Marketing Group in 1998 were followed up in 1999 and 2000. The data were merged with the Dorenfest data on information system integration. A growth curve model was developed and validated by the Mplus statistical program. Factors influencing the top 100 IHNs' performance in 1998 and their subsequent rankings in the consecutive years were analyzed. IHNs' initial performance scores were positively influenced by network size, number of affiliated physicians and profit margin, and were negatively associated with average length of stay and technical efficiency. The continuing high performance, judged by maintaining higher performance scores, tended to be enhanced by the use of more managerial or executive decision-support systems. Future studies should include time-varying operational indicators to serve as predictors of network performance.

  18. A new electromagnetic induction sensor using Vector Network Analyzer technology for accurate characterisation of soil electrical properties

    NASA Astrophysics Data System (ADS)

    André, F.; Lambot, S.; Moghadas, D.; Vereecken, H.

    2009-04-01

    Electromagnetic induction (EMI) has been widely used since the 70s to retrieve soil physico-chemical properties through the measurement of soil electrical conductivity. Soil electrical conductivity integrates several factors, mainly soil water content, salinity, clay content and temperature, and to a lesser extent, mineralogy, porosity, structure, cation exchange capacity, organic matter and bulk density. EMI has been shown to be useful for a wide range of environmental applications. EMI is non invasive and individual measurements are almost instantaneous, which permits to characterise large areas with fine spatial and/or temporal resolutions. Nevertheless, current EMI systems present some limitations. First, EMI usually operates at a single or at a limited number of fixed frequencies, which limits the information that can be retrieved from the subsurface. In addition, the calibration of existing commercial sensors is generally rather empirical and not accurate, which reduces the reliability of the data. Finally, the data processing techniques that are used to retrieve the soil electrical properties from EMI data often rely on strong simplifying assumptions with respect to wave propagation through the antenna-air-soil system. Performing EMI measurements with Vector Network Analyzer (VNA) technology would overcome a part of these limitations, allowing to work simultaneously at a wide range of frequencies and to readily perform robust calibrations, which are defined as an international standard. On that basis, we have developed a new algorithm for off-ground, zero-offset, frequency domain EMI based on full-waveform inverse modelling. The EMI forward model is based on a linear system of complex transfer functions for describing the loop antenna and its interactions with soil and an exact solution of Maxwell's equations for wave propagation in three-dimensional multilayered media. The approach has been validated in laboratory conditions for measurements at different

  19. The Influence of Social Networks on High School Students' Performance

    ERIC Educational Resources Information Center

    Abu-Shanab, Emad; Al-Tarawneh, Heyam

    2015-01-01

    Social networks are becoming an integral part of people's lives. Students are spending much time on social media and are considered the largest category that uses such application. This study tries to explore the influence of social media use, and especially Facebook, on high school students' performance. The study used the GPA of students in four…

  20. Student Performance Assessment Using Bayesian Network and Web Portfolios.

    ERIC Educational Resources Information Center

    Liu, Chen-Chung; Chen, Gwo-Dong; Wang, Chin-Yeh; Lu, Ching-Fang

    2002-01-01

    Proposes a novel methodology that employs Bayesian network software to assist teachers in efficiently deriving and utilizing the student model of activity performance from Web portfolios online. This system contains Web portfolios that record in detail students' learning activities, peer interaction, and knowledge progress. (AEF)

  1. Competitive Learning Neural Network Ensemble Weighted by Predicted Performance

    ERIC Educational Resources Information Center

    Ye, Qiang

    2010-01-01

    Ensemble approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes ensemble performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different…

  2. Comparison of Support Vector Machine, Neural Network, and CART Algorithms for the Land-Cover Classification Using Limited Training Data Points

    EPA Science Inventory

    Support vector machine (SVM) was applied for land-cover characterization using MODIS time-series data. Classification performance was examined with respect to training sample size, sample variability, and landscape homogeneity (purity). The results were compared to two convention...

  3. Performance verification of network function virtualization in software defined optical transport networks

    NASA Astrophysics Data System (ADS)

    Zhao, Yongli; Hu, Liyazhou; Wang, Wei; Li, Yajie; Zhang, Jie

    2017-01-01

    With the continuous opening of resource acquisition and application, there are a large variety of network hardware appliances deployed as the communication infrastructure. To lunch a new network application always implies to replace the obsolete devices and needs the related space and power to accommodate it, which will increase the energy and capital investment. Network function virtualization1 (NFV) aims to address these problems by consolidating many network equipment onto industry standard elements such as servers, switches and storage. Many types of IT resources have been deployed to run Virtual Network Functions (vNFs), such as virtual switches and routers. Then how to deploy NFV in optical transport networks is a of great importance problem. This paper focuses on this problem, and gives an implementation architecture of NFV-enabled optical transport networks based on Software Defined Optical Networking (SDON) with the procedure of vNFs call and return. Especially, an implementation solution of NFV-enabled optical transport node is designed, and a parallel processing method for NFV-enabled OTN nodes is proposed. To verify the performance of NFV-enabled SDON, the protocol interaction procedures of control function virtualization and node function virtualization are demonstrated on SDON testbed. Finally, the benefits and challenges of the parallel processing method for NFV-enabled OTN nodes are simulated and analyzed.

  4. Distribution and larval habitat characterization of Anopheles moucheti, Anopheles nili, and other malaria vectors in river networks of southern Cameroon.

    PubMed

    Antonio-Nkondjio, Christophe; Ndo, Cyrille; Costantini, Carlo; Awono-Ambene, Parfait; Fontenille, Didier; Simard, Frédéric

    2009-12-01

    Despite their importance as malaria vectors, little is known of the bionomic of Anopheles nili and Anopheles moucheti. Larval collections from 24 sites situated along the dense hydrographic network of south Cameroon were examined to assess key ecological factors associated with these mosquitoes distribution in river networks. Morphological identification of the III and IV instar larvae by the use of microscopy revealed that 47.6% of the larvae belong to An. nili and 22.6% to An. moucheti. Five variables were significantly involved with species distribution, the pace of flow of the river (lotic, or lentic), the light exposure (sunny or shady), vegetation (presence or absence of vegetation) the temperature and the presence or absence of debris. Using canonical correspondence analysis, it appeared that lotic rivers, exposed to light, with vegetation or debris were the best predictors of An. nili larval abundance. Whereas, An. moucheti and An. ovengensis were highly associated with lentic rivers, low temperature, having Pistia. An. nili and An. moucheti distribution along river systems across south Cameroon was highly correlated with environmental variables. The distribution of An. nili conforms to that of a generalist species which is adapted to exploiting a variety of environmental conditions, Whereas, An. moucheti, Anopheles ovengensis and Anopheles carnevalei appeared as specialist forest mosquitoes.

  5. 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.

  6. 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?

  7. Performance and optimization of support vector machines in high-energy physics classification problems

    NASA Astrophysics Data System (ADS)

    Sahin, M. Ö.; Krücker, D.; Melzer-Pellmann, I.-A.

    2016-12-01

    In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new-physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery-significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications.

  8. Satellite panicum mosaic virus coat protein enhances the performance of plant virus gene vectors.

    PubMed

    Everett, Anthany L; Scholthof, Herman B; Scholthof, Karen-Beth G

    2010-01-05

    The coat protein of satellite panicum mosaic virus (SPCP) is known to effectively protect its cognate RNA from deleterious events, and here, we tested its stabilizing potential for heterologous virus-based gene vectors in planta. In support of this, a Potato virus X (PVX) vector carrying the SPMV capsid protein (PVX-SPCP) gene was stable for at least three serial systemic passages through Nicotiana benthamiana. To test the effect of SPCP in trans, PVX-SPCP was co-inoculated onto N. benthamiana together with a Tomato bushy stunt virus (TBSV) vector carrying a green fluorescent protein (GFP) gene that normally does not support systemic GFP expression. In contrast, co-inoculation of TBSV-GFP plus PVX-SPCP resulted in GFP accumulation and concomitant green fluorescent spots in upper, non-inoculated leaves in a temperature-responsive manner. These results suggest that the multifaceted SPMV CP has intriguing effects on virus-host interactions that surface in heterologous systems.

  9. Enhanced memory performance thanks to neural network assortativity

    SciTech Connect

    Franciscis, S. de; Johnson, S.; Torres, J. J.

    2011-03-24

    The behaviour of many complex dynamical systems has been found to depend crucially on the structure of the underlying networks of interactions. An intriguing feature of empirical networks is their assortativity--i.e., the extent to which the degrees of neighbouring nodes are correlated. However, until very recently it was difficult to take this property into account analytically, most work being exclusively numerical. We get round this problem by considering ensembles of equally correlated graphs and apply this novel technique to the case of attractor neural networks. Assortativity turns out to be a key feature for memory performance in these systems - so much so that for sufficiently correlated topologies the critical temperature diverges. We predict that artificial and biological neural systems could significantly enhance their robustness to noise by developing positive correlations.

  10. Evaluation of GPFS Connectivity Over High-Performance Networks

    SciTech Connect

    Srinivasan, Jay; Canon, Shane; Andrews, Matthew

    2009-02-17

    We present the results of an evaluation of new features of the latest release of IBM's GPFS filesystem (v3.2). We investigate different ways of connecting to a high-performance GPFS filesystem from a remote cluster using Infiniband (IB) and 10 Gigabit Ethernet. We also examine the performance of the GPFS filesystem with both serial and parallel I/O. Finally, we also present our recommendations for effective ways of utilizing high-bandwidth networks for high-performance I/O to parallel file systems.

  11. Performance evaluation of a routing algorithm based on Hopfield Neural Network for network-on-chip

    NASA Astrophysics Data System (ADS)

    Esmaelpoor, Jamal; Ghafouri, Abdollah

    2015-12-01

    Network on chip (NoC) has emerged as a solution to overcome the system on chip growing complexity and design challenges. A proper routing algorithm is a key issue of an NoC design. An appropriate routing method balances load across the network channels and keeps path length as short as possible. This survey investigates the performance of a routing algorithm based on Hopfield Neural Network. It is a dynamic programming to provide optimal path and network monitoring in real time. The aim of this article is to analyse the possibility of using a neural network as a router. The algorithm takes into account the path with the lowest delay (cost) form source to destination. In other words, the path a message takes from source to destination depends on network traffic situation at the time and it is the fastest one. The simulation results show that the proposed approach improves average delay, throughput and network congestion efficiently. At the same time, the increase in power consumption is almost negligible.

  12. On the Performance of TCP Spoofing in Satellite Networks

    NASA Technical Reports Server (NTRS)

    Ishac, Joseph; Allman, Mark

    2001-01-01

    In this paper, we analyze the performance of Transmission Control Protocol (TCP) in a network that consists of both satellite and terrestrial components. One method, proposed by outside research, to improve the performance of data transfers over satellites is to use a performance enhancing proxy often dubbed 'spoofing.' Spoofing involves the transparent splitting of a TCP connection between the source and destination by some entity within the network path. In order to analyze the impact of spoofing, we constructed a simulation suite based around the network simulator ns-2. The simulation reflects a host with a satellite connection to the Internet and allows the option to spoof connections just prior to the satellite. The methodology used in our simulation allows us to analyze spoofing over a large range of file sizes and under various congested conditions, while prior work on this topic has primarily focused on bulk transfers with no congestion. As a result of these simulations, we find that the performance of spoofing is dependent upon a number of conditions.

  13. Design and implementation of a high performance network security processor

    NASA Astrophysics Data System (ADS)

    Wang, Haixin; Bai, Guoqiang; Chen, Hongyi

    2010-03-01

    The last few years have seen many significant progresses in the field of application-specific processors. One example is network security processors (NSPs) that perform various cryptographic operations specified by network security protocols and help to offload the computation intensive burdens from network processors (NPs). This article presents a high performance NSP system architecture implementation intended for both internet protocol security (IPSec) and secure socket layer (SSL) protocol acceleration, which are widely employed in virtual private network (VPN) and e-commerce applications. The efficient dual one-way pipelined data transfer skeleton and optimised integration scheme of the heterogenous parallel crypto engine arrays lead to a Gbps rate NSP, which is programmable with domain specific descriptor-based instructions. The descriptor-based control flow fragments large data packets and distributes them to the crypto engine arrays, which fully utilises the parallel computation resources and improves the overall system data throughput. A prototyping platform for this NSP design is implemented with a Xilinx XC3S5000 based FPGA chip set. Results show that the design gives a peak throughput for the IPSec ESP tunnel mode of 2.85 Gbps with over 2100 full SSL handshakes per second at a clock rate of 95 MHz.

  14. Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy

    SciTech Connect

    Jayasurya, K.; Fung, G.; Yu, S.; Dehing-Oberije, C.; De Ruysscher, D.; Hope, A.; De Neve, W.; Lievens, Y.; Lambin, P.; Dekker, A. L. A. J.

    2010-04-15

    Purpose: Classic statistical and machine learning models such as support vector machines (SVMs) can be used to predict cancer outcome, but often only perform well if all the input variables are known, which is unlikely in the medical domain. Bayesian network (BN) models have a natural ability to reason under uncertainty and might handle missing data better. In this study, the authors hypothesize that a BN model can predict two-year survival in non-small cell lung cancer (NSCLC) patients as accurately as SVM, but will predict survival more accurately when data are missing. Methods: A BN and SVM model were trained on 322 inoperable NSCLC patients treated with radiotherapy from Maastricht and validated in three independent data sets of 35, 47, and 33 patients from Ghent, Leuven, and Toronto. Missing variables occurred in the data set with only 37, 28, and 24 patients having a complete data set. Results: The BN model structure and parameter learning identified gross tumor volume size, performance status, and number of positive lymph nodes on a PET as prognostic factors for two-year survival. When validated in the full validation set of Ghent, Leuven, and Toronto, the BN model had an AUC of 0.77, 0.72, and 0.70, respectively. A SVM model based on the same variables had an overall worse performance (AUC 0.71, 0.68, and 0.69) especially in the Ghent set, which had the highest percentage of missing the important GTV size data. When only patients with complete data sets were considered, the BN and SVM model performed more alike. Conclusions: Within the limitations of this study, the hypothesis is supported that BN models are better at handling missing data than SVM models and are therefore more suitable for the medical domain. Future works have to focus on improving the BN performance by including more patients, more variables, and more diversity.

  15. Copercolating Networks: An Approach for Realizing High-Performance Transparent Conductors using Multicomponent Nanostructured Networks

    NASA Astrophysics Data System (ADS)

    Das, Suprem R.; Sadeque, Sajia; Jeong, Changwook; Chen, Ruiyi; Alam, Muhammad A.; Janes, David B.

    2016-06-01

    Although transparent conductive oxides such as indium tin oxide (ITO) are widely employed as transparent conducting electrodes (TCEs) for applications such as touch screens and displays, new nanostructured TCEs are of interest for future applications, including emerging transparent and flexible electronics. A number of twodimensional networks of nanostructured elements have been reported, including metallic nanowire networks consisting of silver nanowires, metallic carbon nanotubes (m-CNTs), copper nanowires or gold nanowires, and metallic mesh structures. In these single-component systems, it has generally been difficult to achieve sheet resistances that are comparable to ITO at a given broadband optical transparency. A relatively new third category of TCEs consisting of networks of 1D-1D and 1D-2D nanocomposites (such as silver nanowires and CNTs, silver nanowires and polycrystalline graphene, silver nanowires and reduced graphene oxide) have demonstrated TCE performance comparable to, or better than, ITO. In such hybrid networks, copercolation between the two components can lead to relatively low sheet resistances at nanowire densities corresponding to high optical transmittance. This review provides an overview of reported hybrid networks, including a comparison of the performance regimes achievable with those of ITO and single-component nanostructured networks. The performance is compared to that expected from bulk thin films and analyzed in terms of the copercolation model. In addition, performance characteristics relevant for flexible and transparent applications are discussed. The new TCEs are promising, but significant work must be done to ensure earth abundance, stability, and reliability so that they can eventually replace traditional ITO-based transparent conductors.

  16. Overhead-Performance Tradeoffs in Distributed Wireless Networks

    DTIC Science & Technology

    2015-06-26

    time-frequency resources are spent on non-information bearing control information that is not efficiently encoded. Three simple resource controllers...poorly encoded non-information bearing resource measurement and control signals. This enabled the investigators to make a strong case for studying the...between the overhead an optimized distributed wireless network controller collects and the performance on the data- bearing signals it achieves: the more

  17. Radar target classification method with high accuracy and decision speed performance using MUSIC spectrum vectors and PCA projection

    NASA Astrophysics Data System (ADS)

    Secmen, Mustafa

    2011-10-01

    This paper introduces the performance of an electromagnetic target recognition method in resonance scattering region, which includes pseudo spectrum Multiple Signal Classification (MUSIC) algorithm and principal component analysis (PCA) technique. The aim of this method is to classify an "unknown" target as one of the "known" targets in an aspect-independent manner. The suggested method initially collects the late-time portion of noise-free time-scattered signals obtained from different reference aspect angles of known targets. Afterward, these signals are used to obtain MUSIC spectrums in real frequency domain having super-resolution ability and noise resistant feature. In the final step, PCA technique is applied to these spectrums in order to reduce dimensionality and obtain only one feature vector per known target. In the decision stage, noise-free or noisy scattered signal of an unknown (test) target from an unknown aspect angle is initially obtained. Subsequently, MUSIC algorithm is processed for this test signal and resulting test vector is compared with feature vectors of known targets one by one. Finally, the highest correlation gives the type of test target. The method is applied to wire models of airplane targets, and it is shown that it can tolerate considerable noise levels although it has a few different reference aspect angles. Besides, the runtime of the method for a test target is sufficiently low, which makes the method suitable for real-time applications.

  18. Parallel access alignment network with barrel switch implementation for d-ordered vector elements

    NASA Technical Reports Server (NTRS)

    Barnes, George H. (Inventor)

    1980-01-01

    An alignment network between N parallel data input ports and N parallel data outputs includes a first and a second barrel switch. The first barrel switch fed by the N parallel input ports shifts the N outputs thereof and in turn feeds the N-1 input data paths of the second barrel switch according to the relationship X=k.sup.y modulo N wherein x represents the output data path ordering of the first barrel switch, y represents the input data path ordering of the second barrel switch, and k equals a primitive root of the number N. The zero (0) ordered output data path of the first barrel switch is fed directly to the zero ordered output port. The N-1 output data paths of the second barrel switch are connected to the N output ports in the reverse ordering of the connections between the output data paths of the first barrel switch and the input data paths of the second barrel switch. The second switch is controlled by a value m, which in the preferred embodiment is produced at the output of a ROM addressed by the value d wherein d represents the incremental spacing or distance between data elements to be accessed from the N input ports, and m is generated therefrom according to the relationship d=k.sup.m modulo N.

  19. Sensor Networking Testbed with IEEE 1451 Compatibility and Network Performance Monitoring

    NASA Technical Reports Server (NTRS)

    Gurkan, Deniz; Yuan, X.; Benhaddou, D.; Figueroa, F.; Morris, Jonathan

    2007-01-01

    Design and implementation of a testbed for testing and verifying IEEE 1451-compatible sensor systems with network performance monitoring is of significant importance. The performance parameters measurement as well as decision support systems implementation will enhance the understanding of sensor systems with plug-and-play capabilities. The paper will present the design aspects for such a testbed environment under development at University of Houston in collaboration with NASA Stennis Space Center - SSST (Smart Sensor System Testbed).

  20. Design and Performance Analysis of Incremental Networked Predictive Control Systems.

    PubMed

    Pang, Zhong-Hua; Liu, Guo-Ping; Zhou, Donghua

    2016-06-01

    This paper is concerned with the design and performance analysis of networked control systems with network-induced delay, packet disorder, and packet dropout. Based on the incremental form of the plant input-output model and an incremental error feedback control strategy, an incremental networked predictive control (INPC) scheme is proposed to actively compensate for the round-trip time delay resulting from the above communication constraints. The output tracking performance and closed-loop stability of the resulting INPC system are considered for two cases: 1) plant-model match case and 2) plant-model mismatch case. For the former case, the INPC system can achieve the same output tracking performance and closed-loop stability as those of the corresponding local control system. For the latter case, a sufficient condition for the stability of the closed-loop INPC system is derived using the switched system theory. Furthermore, for both cases, the INPC system can achieve a zero steady-state output tracking error for step commands. Finally, both numerical simulations and practical experiments on an Internet-based servo motor system illustrate the effectiveness of the proposed method.

  1. Network Performance Measurements for NASA's Earth Observation System

    NASA Technical Reports Server (NTRS)

    Loiacono, Joe; Gormain, Andy; Smith, Jeff

    2004-01-01

    NASA's Earth Observation System (EOS) Project studies all aspects of planet Earth from space, including climate change, and ocean, ice, land, and vegetation characteristics. It consists of about 20 satellite missions over a period of about a decade. Extensive collaboration is used, both with other US. agencies (e.g., National Oceanic and Atmospheric Administration (NOA), United States Geological Survey (USGS), Department of Defense (DoD), and international agencies (e.g., European Space Agency (ESA), Japan Aerospace Exploration Agency (JAXA)), to improve cost effectiveness and obtain otherwise unavailable data. Scientific researchers are located at research institutions worldwide, primarily government research facilities and research universities. The EOS project makes extensive use of networks to support data acquisition, data production, and data distribution. Many of these functions impose requirements on the networks, including throughput and availability. In order to verify that these requirements are being met, and be pro-active in recognizing problems, NASA conducts on-going performance measurements. The purpose of this paper is to examine techniques used by NASA to measure the performance of the networks used by EOSDIS (EOS Data and Information System) and to indicate how this performance information is used.

  2. The Algerian Seismic Network: Performance from data quality analysis

    NASA Astrophysics Data System (ADS)

    Yelles, Abdelkarim; Allili, Toufik; Alili, Azouaou

    2013-04-01

    densify the network and to enhance performance of the Algerian Digital Seismic Network.

  3. 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

  4. Neural net approach to predictive vector quantization

    NASA Astrophysics Data System (ADS)

    Mohsenian, Nader; Nasrabadi, Nasser M.

    1992-11-01

    A new predictive vector quantization (PVQ) technique, capable of exploring the nonlinear dependencies in addition to the linear dependencies that exist between adjacent blocks of pixels, is introduced. Two different classes of neural nets form the components of the PVQ scheme. A multi-layer perceptron is embedded in the predictive component of the compression system. This neural network, using the non-linearity condition associated with its processing units, can perform as a non-linear vector predictor. The second component of the PVQ scheme vector quantizes (VQ) the residual vector that is formed by subtracting the output of the perceptron from the original wave-pattern. Kohonen Self-Organizing Feature Map (KSOFM) was utilized as a neural network clustering algorithm to design the codebook for the VQ technique. Coding results are presented for monochrome 'still' images.

  5. Study of Fe/Cr Magnetic Multilayers and Periodic Arrays of Submicron Magnetic Dots by Vector Network Analyzer Technique

    NASA Astrophysics Data System (ADS)

    Aliev, Farkhad; Francisco Sierra, Juan; Awad, Ahmad; Pryadun, Vladimir; Kakazei, Gleb

    2008-03-01

    Vector network analyzer (VNA) technique up to 8.5 GHz was applied to measure in-plane dynamic response in Fe/Cr magnetic multilayers and for the in-plane magnetized periodic arrays of Permalloy circular magnetic dots. In the antiferromagnetically coupled [Fe/Cr]n multilayers (n=10,20,40) we have investigated field dependence of the acoustic resonance in a wide range of temperatures between 300K down to 2K both for the low magnetic fields and close to the saturation field. FMR studies of the array of FeNi dots with diameter of 1 micron, the aspect ratio L/R=0.1 and with centre to centre distance varying between 1.2 to 2.5 micron allowed to resolve multiple FMR resonances as a function of magnetic field. We have found the main FMR linewidth to be dependent on the magnetic history. For the magnetic fields below 300 Oe, where magnetic vortex state forms, we have observed the field dependence of the radial modes (fr > 6GHz) to show minima close to the zero magnetic field.

  6. Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine

    NASA Astrophysics Data System (ADS)

    Ebrahimi, Hadi; Rajaee, Taher

    2017-01-01

    Simulation of groundwater level (GWL) fluctuations is an important task in management of groundwater resources. In this study, the effect of wavelet analysis on the training of the artificial neural network (ANN), multi linear regression (MLR) and support vector regression (SVR) approaches was investigated, and the ANN, MLR and SVR along with the wavelet-ANN (WNN), wavelet-MLR (WLR) and wavelet-SVR (WSVR) models were compared in simulating one-month-ahead of GWL. The only variable used to develop the models was the monthly GWL data recorded over a period of 11 years from two wells in the Qom plain, Iran. The results showed that decomposing GWL time series into several sub-time series, extremely improved the training of the models. For both wells 1 and 2, the Meyer and Db5 wavelets produced better results compared to the other wavelets; which indicated wavelet types had similar behavior in similar case studies. The optimal number of delays was 6 months, which seems to be due to natural phenomena. The best WNN model, using Meyer mother wavelet with two decomposition levels, simulated one-month-ahead with RMSE values being equal to 0.069 m and 0.154 m for wells 1 and 2, respectively. The RMSE values for the WLR model were 0.058 m and 0.111 m, and for WSVR model were 0.136 m and 0.060 m for wells 1 and 2, respectively.

  7. Coexistence: Threat to the Performance of Heterogeneous Network

    NASA Astrophysics Data System (ADS)

    Sharma, Neetu; Kaur, Amanpreet

    2010-11-01

    Wireless technology is gaining broad acceptance as users opt for the freedom that only wireless network can provide. Well-accepted wireless communication technologies generally operate in frequency bands that are shared among several users, often using different RF schemes. This is true in particular for WiFi, Bluetooth, and more recently ZigBee. These all three operate in the unlicensed 2.4 GHz band, also known as ISM band, which has been key to the development of a competitive and innovative market for wireless embedded devices. But, as with any resource held in common, it is crucial that those technologies coexist peacefully to allow each user of the band to fulfill its communication goals. This has led to an increase in wireless devices intended for use in IEEE 802.11 wireless local area networks (WLANs) and wireless personal area networks (WPANs), both of which support operation in the crowded 2.4-GHz industrial, scientific and medical (ISM) band. Despite efforts made by standardization bodies to ensure smooth coexistence it may occur that communication technologies transmitting for instance at very different power levels interfere with each other. In particular, it has been pointed out that ZigBee could potentially experience interference from WiFi traffic given that while both protocols can transmit on the same channel, WiFi transmissions usually occur at much higher power level. In this work, we considered a heterogeneous network and analyzed the impact of coexistence between IEEE 802.15.4 and IEEE 802.11b. To evaluate the performance of this network, measurement and simulation study are conducted and developed in the QualNet Network simulator, version 5.0.Model is analyzed for different placement models or topologies such as Random. Grid & Uniform. Performance is analyzed on the basis of characteristics such as throughput, average jitter and average end to end delay. Here, the impact of varying different antenna gain & shadowing model for this

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

    PubMed

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

    2016-08-23

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

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

    PubMed Central

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

    2016-01-01

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

  10. Road safety performance indicators for the interurban road network.

    PubMed

    Yannis, George; Weijermars, Wendy; Gitelman, Victoria; Vis, Martijn; Chaziris, Antonis; Papadimitriou, Eleonora; Azevedo, Carlos Lima

    2013-11-01

    Various road safety performance indicators (SPIs) have been proposed for different road safety research areas, mainly as regards driver behaviour (e.g. seat belt use, alcohol, drugs, etc.) and vehicles (e.g. passive safety); however, no SPIs for the road network and design have been developed. The objective of this research is the development of an SPI for the road network, to be used as a benchmark for cross-region comparisons. The developed SPI essentially makes a comparison of the existing road network to the theoretically required one, defined as one which meets some minimum requirements with respect to road safety. This paper presents a theoretical concept for the determination of this SPI as well as a translation of this theory into a practical method. Also, the method is applied in a number of pilot countries namely the Netherlands, Portugal, Greece and Israel. The results show that the SPI could be efficiently calculated in all countries, despite some differences in the data sources. In general, the calculated overall SPI scores were realistic and ranged from 81 to 94%, with the exception of Greece where the SPI was relatively lower (67%). However, the SPI should be considered as a first attempt to determine the safety level of the road network. The proposed method has some limitations and could be further improved. The paper presents directions for further research to further develop the SPI.

  11. Performance evaluation of distributed wavelength assignment in WDM optical networks

    NASA Astrophysics Data System (ADS)

    Hashiguchi, Tomohiro; Wang, Xi; Morikawa, Hiroyuki; Aoyama, Tomonori

    2004-04-01

    In WDM wavelength routed networks, prior to a data transfer, a call setup procedure is required to reserve a wavelength path between the source-destination node pairs. A distributed approach to a connection setup can achieve a very high speed, while improving the reliability and reducing the implementation cost of the networks. However, along with many advantages, several major challenges have been posed by the distributed scheme in how the management and allocation of wavelength could be efficiently carried out. In this thesis, we apply a distributed wavelength assignment algorithm named priority based wavelength assignment (PWA) that was originally proposed for the use in burst switched optical networks to the problem of reserving wavelengths of path reservation protocols in the distributed control optical networks. Instead of assigning wavelengths randomly, this approach lets each node select the "safest" wavelengths based on the information of wavelength utilization history, thus unnecessary future contention is prevented. The simulation results presented in this paper show that the proposed protocol can enhance the performance of the system without introducing any apparent drawbacks.

  12. Social value of high bandwidth networks: creative performance and education.

    PubMed

    Mansell, Robin; Foresta, Don

    2016-03-06

    This paper considers limitations of existing network technologies for distributed theatrical performance in the creative arts and for symmetrical real-time interaction in online learning environments. It examines the experience of a multidisciplinary research consortium that aimed to introduce a solution to latency and other network problems experienced by users in these sectors. The solution builds on the Multicast protocol, Access Grid, an environment supported by very high bandwidth networks. The solution is intended to offer high-quality image and sound, interaction with other network platforms, maximum user control of multipoint transmissions, and open programming tools that are flexible and modifiable for specific uses. A case study is presented drawing upon an extended period of participant observation by the authors. This provides a basis for an examination of the challenges of promoting technological innovation in a multidisciplinary project. We highlight the kinds of technical advances and cultural and organizational changes that would be required to meet demanding quality standards, the way a research consortium planned to engage in experimentation and learning, and factors making it difficult to achieve an open platform that is responsive to the needs of users in the creative arts and education sectors.

  13. OPTIMAL CONFIGURATION OF A COMMAND AND CONTROL NETWORK: BALANCING PERFORMANCE AND RECONFIGURATION CONSTRAINTS

    SciTech Connect

    L. DOWELL

    1999-08-01

    The optimization of the configuration of communications and control networks is important for assuring the reliability and performance of the networks. This paper presents techniques for determining the optimal configuration for such a network in the presence of communication and connectivity constraints. reconfiguration to restore connectivity to a data-fusion network following the failure of a network component.

  14. On the Classification Performance of TAN and General Bayesian Networks

    NASA Astrophysics Data System (ADS)

    Madden, Michael G.

    Over a decade ago, Friedmanet al. introduced the Tree Augmented Naïve Bayes (TAN) classifier, with experiments indicating that it significantly outperformed Naïve Bayes (NB) in terms of classification accuracy, whereas general Bayesian network (GBN) classifiers performed no better than NB. This paper challenges those claims, using a careful experimental analysis to show that GBN classifiers significantly outperform NB on datasets analyzed, and are comparable to TAN performance. It is found that the poor performance reported by Friedman et al. are not attributable to the GBN per se, but rather to their use of simple empirical frequencies to estimate GBN parameters, whereas basic parameter smoothing (used in their TAN analyses but not their GBN analyses) improves GBN performance significantly. It is concluded that, while GBN classifiers may have some limitations, they deserve greater attention, particularly in domains where insight into classification decisions, as well as good accuracy, is required.

  15. Digitally controlled high-performance dc SQUID readout electronics for a 304-channel vector magnetometer

    NASA Astrophysics Data System (ADS)

    Bechstein, S.; Petsche, F.; Scheiner, M.; Drung, D.; Thiel, F.; Schnabel, A.; Schurig, Th

    2006-06-01

    Recently, we have developed a family of dc superconducting quantum interference device (SQUID) readout electronics for several applications. These electronics comprise a low-noise preamplifier followed by an integrator, and an analog SQUID bias circuit. A highly-compact low-power version with a flux-locked loop bandwidth of 0.3 MHz and a white noise level of 1 nV/√Hz was specially designed for a 304-channel low-Tc dc SQUID vector magnetometer, intended to operate in the new Berlin Magnetically Shielded Room (BMSR-2). In order to minimize the space needed to mount the electronics on top of the dewar and to minimize the power consumption, we have integrated four electronics channels on one 3 cm × 10 cm sized board. Furthermore we embedded the analog components of these four channels into a digitally controlled system including an in-system programmable microcontroller. Four of these integrated boards were combined to one module with a size of 4 cm × 4 cm × 16 cm. 19 of these modules were implemented, resulting in a total power consumption of about 61 W. To initialize the 304 channels and to service the system we have developed software tools running on a laptop computer. By means of these software tools the microcontrollers are fed with all required data such as the working points, the characteristic parameters of the sensors (noise, voltage swing), or the sensor position inside of the vector magnetometer system. In this paper, the developed electronics including the software tools are described, and first results are presented.

  16. Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM).

    PubMed

    Fernandez, Michael; Caballero, Julio; Fernandez, Leyden; Sarai, Akinori

    2011-02-01

    Many articles in "in silico" drug design implemented genetic algorithm (GA) for feature selection, model optimization, conformational search, or docking studies. Some of these articles described GA applications to quantitative structure-activity relationships (QSAR) modeling in combination with regression and/or classification techniques. We reviewed the implementation of GA in drug design QSAR and specifically its performance in the optimization of robust mathematical models such as Bayesian-regularized artificial neural networks (BRANNs) and support vector machines (SVMs) on different drug design problems. Modeled data sets encompassed ADMET and solubility properties, cancer target inhibitors, acetylcholinesterase inhibitors, HIV-1 protease inhibitors, ion-channel and calcium entry blockers, and antiprotozoan compounds as well as protein classes, functional, and conformational stability data. The GA-optimized predictors were often more accurate and robust than previous published models on the same data sets and explained more than 65% of data variances in validation experiments. In addition, feature selection over large pools of molecular descriptors provided insights into the structural and atomic properties ruling ligand-target interactions.

  17. A case study using support vector machines, neural networks and logistic regression in a GIS to identify wells contaminated with nitrate-N

    NASA Astrophysics Data System (ADS)

    Dixon, Barnali

    2009-09-01

    Accurate and inexpensive identification of potentially contaminated wells is critical for water resources protection and management. The objectives of this study are to 1) assess the suitability of approximation tools such as neural networks (NN) and support vector machines (SVM) integrated in a geographic information system (GIS) for identifying contaminated wells and 2) use logistic regression and feature selection methods to identify significant variables for transporting contaminants in and through the soil profile to the groundwater. Fourteen GIS derived soil hydrogeologic and landuse parameters were used as initial inputs in this study. Well water quality data (nitrate-N) from 6,917 wells provided by Florida Department of Environmental Protection (USA) were used as an output target class. The use of the logistic regression and feature selection methods reduced the number of input variables to nine. Receiver operating characteristics (ROC) curves were used for evaluation of these approximation tools. Results showed superior performance with the NN as compared to SVM especially on training data while testing results were comparable. Feature selection did not improve accuracy; however, it helped increase the sensitivity or true positive rate (TPR). Thus, a higher TPR was obtainable with fewer variables.

  18. A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection.

    PubMed

    Delaney, Declan T; O'Hare, Gregory M P

    2016-12-01

    No single network solution for Internet of Things (IoT) networks can provide the required level of Quality of Service (QoS) for all applications in all environments. This leads to an increasing number of solutions created to fit particular scenarios. Given the increasing number and complexity of solutions available, it becomes difficult for an application developer to choose the solution which is best suited for an application. This article introduces a framework which autonomously chooses the best solution for the application given the current deployed environment. The framework utilises a performance model to predict the expected performance of a particular solution in a given environment. The framework can then choose an apt solution for the application from a set of available solutions. This article presents the framework with a set of models built using data collected from simulation. The modelling technique can determine with up to 85% accuracy the solution which performs the best for a particular performance metric given a set of solutions. The article highlights the fractured and disjointed practice currently in place for examining and comparing communication solutions and aims to open a discussion on harmonising testing procedures so that different solutions can be directly compared and offers a framework to achieve this within IoT networks.

  19. A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection †

    PubMed Central

    Delaney, Declan T.; O’Hare, Gregory M. P.

    2016-01-01

    No single network solution for Internet of Things (IoT) networks can provide the required level of Quality of Service (QoS) for all applications in all environments. This leads to an increasing number of solutions created to fit particular scenarios. Given the increasing number and complexity of solutions available, it becomes difficult for an application developer to choose the solution which is best suited for an application. This article introduces a framework which autonomously chooses the best solution for the application given the current deployed environment. The framework utilises a performance model to predict the expected performance of a particular solution in a given environment. The framework can then choose an apt solution for the application from a set of available solutions. This article presents the framework with a set of models built using data collected from simulation. The modelling technique can determine with up to 85% accuracy the solution which performs the best for a particular performance metric given a set of solutions. The article highlights the fractured and disjointed practice currently in place for examining and comparing communication solutions and aims to open a discussion on harmonising testing procedures so that different solutions can be directly compared and offers a framework to achieve this within IoT networks. PMID:27916929

  20. High-performance, scalable optical network-on-chip architectures

    NASA Astrophysics Data System (ADS)

    Tan, Xianfang

    The rapid advance of technology enables a large number of processing cores to be integrated into a single chip which is called a Chip Multiprocessor (CMP) or a Multiprocessor System-on-Chip (MPSoC) design. The on-chip interconnection network, which is the communication infrastructure for these processing cores, plays a central role in a many-core system. With the continuously increasing complexity of many-core systems, traditional metallic wired electronic networks-on-chip (NoC) became a bottleneck because of the unbearable latency in data transmission and extremely high energy consumption on chip. Optical networks-on-chip (ONoC) has been proposed as a promising alternative paradigm for electronic NoC with the benefits of optical signaling communication such as extremely high bandwidth, negligible latency, and low power consumption. This dissertation focus on the design of high-performance and scalable ONoC architectures and the contributions are highlighted as follow: 1. A micro-ring resonator (MRR)-based Generic Wavelength-routed Optical Router (GWOR) is proposed. A method for developing any sized GWOR is introduced. GWOR is a scalable non-blocking ONoC architecture with simple structure, low cost and high power efficiency compared to existing ONoC designs. 2. To expand the bandwidth and improve the fault tolerance of the GWOR, a redundant GWOR architecture is designed by cascading different type of GWORs into one network. 3. The redundant GWOR built with MRR-based comb switches is proposed. Comb switches can expand the bandwidth while keep the topology of GWOR unchanged by replacing the general MRRs with comb switches. 4. A butterfly fat tree (BFT)-based hybrid optoelectronic NoC (HONoC) architecture is developed in which GWORs are used for global communication and electronic routers are used for local communication. The proposed HONoC uses less numbers of electronic routers and links than its counterpart of electronic BFT-based NoC. It takes the advantages of

  1. Performance characteristics of omnidirectional antennas for spacecraft using NASA networks

    NASA Technical Reports Server (NTRS)

    Hilliard, Lawrence M.

    1987-01-01

    Described are the performance capabilities and critical elements of the shaped omni antenna developed for NASA for space users of NASA networks. The shaped omni is designed to be operated in tandem for virtually omnidirectional coverage and uniform gain free of spacecraft interference. These antennas are ideal for low gain data requirements and emergency backup, deployment, amd retrieval of higher gain RF systems. Other omnidirectional antennas that have flown in space are described in the final section. A performance summary for the shaped omni is in the Appendix. This document introduces organizations and projects to the shaped omni applications for NASA's space use. Coverage, gain, weight, power, and implementation and other performance information for satisfying a wide range of data requirements are included.

  2. Mean-square exponential input-to-state stability of delayed Cohen-Grossberg neural networks with Markovian switching based on vector Lyapunov functions.

    PubMed

    Li, Zhihong; Liu, Lei; Zhu, Quanxin

    2016-12-01

    This paper studies the mean-square exponential input-to-state stability of delayed Cohen-Grossberg neural networks with Markovian switching. By using the vector Lyapunov function and property of M-matrix, two generalized Halanay inequalities are established. By means of the generalized Halanay inequalities, sufficient conditions are also obtained, which can ensure the exponential input-to-state stability of delayed Cohen-Grossberg neural networks with Markovian switching. Two numerical examples are given to illustrate the efficiency of the derived results.

  3. Vector Reflectometry in a Beam Waveguide

    NASA Technical Reports Server (NTRS)

    Eimer, J. R.; Bennett, C. L.; Chuss, D. T.; Wollack, E. J.

    2011-01-01

    We present a one-port calibration technique for characterization of beam waveguide components with a vector network analyzer. This technique involves using a set of known delays to separate the responses of the instrument and the device under test. We demonstrate this technique by measuring the reflected performance of a millimeter-wave variable-delay polarization modulator.

  4. Network and User-Perceived Performance of Web Page Retrievals

    NASA Technical Reports Server (NTRS)

    Kruse, Hans; Allman, Mark; Mallasch, Paul

    1998-01-01

    The development of the HTTP protocol has been driven by the need to improve the network performance of the protocol by allowing the efficient retrieval of multiple parts of a web page without the need for multiple simultaneous TCP connections between a client and a server. We suggest that the retrieval of multiple page elements sequentially over a single TCP connection may result in a degradation of the perceived performance experienced by the user. We attempt to quantify this perceived degradation through the use of a model which combines a web retrieval simulation and an analytical model of TCP operation. Starting with the current HTTP/l.1 specification, we first suggest a client@side heuristic to improve the perceived transfer performance. We show that the perceived speed of the page retrieval can be increased without sacrificing data transfer efficiency. We then propose a new client/server extension to the HTTP/l.1 protocol to allow for the interleaving of page element retrievals. We finally address the issue of the display of advertisements on web pages, and in particular suggest a number of mechanisms which can make efficient use of IP multicast to send advertisements to a number of clients within the same network.

  5. High-Performance, Semi-Interpenetrating Polymer Network

    NASA Technical Reports Server (NTRS)

    Pater, Ruth H.; Lowther, Sharon E.; Smith, Janice Y.; Cannon, Michelle S.; Whitehead, Fred M.; Ely, Robert M.

    1992-01-01

    High-performance polymer made by new synthesis in which one or more easy-to-process, but brittle, thermosetting polyimides combined with one or more tough, but difficult-to-process, linear thermoplastics to yield semi-interpenetrating polymer network (semi-IPN) having combination of easy processability and high tolerance to damage. Two commercially available resins combined to form tough, semi-IPN called "LaRC-RP49." Displays improvements in toughness and resistance to microcracking. LaRC-RP49 has potential as high-temperature matrix resin, adhesive, and molding resin. Useful in aerospace, automotive, and electronic industries.

  6. Performance evaluation for epileptic electroencephalogram (EEG) detection by using Neyman-Pearson criteria and a support vector machine

    NASA Astrophysics Data System (ADS)

    Wang, Chun-mei; Zhang, Chong-ming; Zou, Jun-zhong; Zhang, Jian

    2012-02-01

    The diagnosis of several neurological disorders is based on the detection of typical pathological patterns in electroencephalograms (EEGs). This is a time-consuming task requiring significant training and experience. A lot of effort has been devoted to developing automatic detection techniques which might help not only in accelerating this process but also in avoiding the disagreement among readers of the same record. In this work, Neyman-Pearson criteria and a support vector machine (SVM) are applied for detecting an epileptic EEG. Decision making is performed in two stages: feature extraction by computing the wavelet coefficients and the approximate entropy (ApEn) and detection by using Neyman-Pearson criteria and an SVM. Then the detection performance of the proposed method is evaluated. Simulation results demonstrate that the wavelet coefficients and the ApEn are features that represent the EEG signals well. By comparison with Neyman-Pearson criteria, an SVM applied on these features achieved higher detection accuracies.

  7. Supporting Proactive Application Event Notification to Improve Sensor Network Performance

    NASA Astrophysics Data System (ADS)

    Merlin, Christophe J.; Heinzelman, Wendi B.

    As wireless sensor networks gain in popularity, many deployments are posing new challenges due to their diverse topologies and resource constraints. Previous work has shown the advantage of adapting protocols based on current network conditions (e.g., link status, neighbor status), in order to provide the best service in data transport. Protocols can similarly benefit from adaptation based on current application conditions. In particular, if proactively informed of the status of active queries in the network, protocols can adjust their behavior accordingly. In this paper, we propose a novel approach to provide such proactive application event notification to all interested protocols in the stack. Specifically, we use the existing interfaces and event signaling structure provided by the X-Lisa (Cross-layer Information Sharing Architecture) protocol architecture, augmenting this architecture with a Middleware Interpreter for managing application queries and performing event notification. Using this approach, we observe gains in Quality of Service of up to 40% in packet delivery ratios and a 75% decrease in packet delivery delay for the tested scenario.

  8. Deploying optical performance monitoring in TeliaSonera's network

    NASA Astrophysics Data System (ADS)

    Svensson, Torbjorn K.; Karlsson, Per-Olov E.

    2004-09-01

    This paper reports on the first steps taken by TeliaSonera towards deploying optical performance monitoring (OPM) in the company"s transport network, in order to assure increasingly reliable communications on the physical layer. The big leap, a world-wide deployment of OPM still awaits a breakthrough. There is required very obvious benefits from using OPM in order to change this stalemate. Reasons may be the anaemic economy of many telecom operators, shareholders" pushing for short-term payback, and reluctance to add complexity and to integrate a system management. Technically, legacy digital systems do already have a proven ability of monitoring, so adding OPM to the dense wavelength division multiplexed (DWDM) systems in operation should be judged with care. Duly installed, today"s DWDM systems do their job well, owing to rigorous rules for link design and a prosperous power budget, a power management inherent to the system, and a reliable supplier"s support. So what may bring this stalemate to an end? -A growing number of appliances of OPM, for enhancing network operation and maintenance, and enabling new customer services, will most certainly bring momentum to a change. The first employment of OPM in TeliaSonera"s network is launched this year, 2004. The preparedness of future OPM dependent services and transport technologies will thereby be granted.

  9. The challenges of archiving networked-based multimedia performances (Performance cryogenics)

    NASA Astrophysics Data System (ADS)

    Cohen, Elizabeth; Cooperstock, Jeremy; Kyriakakis, Chris

    2002-11-01

    Music archives and libraries have cultural preservation at the core of their charters. New forms of art often race ahead of the preservation infrastructure. The ability to stream multiple synchronized ultra-low latency streams of audio and video across a continent for a distributed interactive performance such as music and dance with high-definition video and multichannel audio raises a series of challenges for the architects of digital libraries and those responsible for cultural preservation. The archiving of such performances presents numerous challenges that go beyond simply recording each stream. Case studies of storage and subsequent retrieval issues for Internet2 collaborative performances are discussed. The development of shared reality and immersive environments generate issues about, What constitutes an archived performance that occurs across a network (in multiple spaces over time)? What are the families of necessary metadata to reconstruct this virtual world in another venue or era? For example, if the network exhibited changes in latency the performers most likely adapted. In a future recreation, the latency will most likely be completely different. We discuss the parameters of immersive environment acquisition and rendering, network architectures, software architecture, musical/choreographic scores, and environmental acoustics that must be considered to address this problem.

  10. Observed and predicted performance of the global IMS infrasound network

    NASA Astrophysics Data System (ADS)

    Le Pichon, A.; Ceranna, L.; Landes, M.

    2012-04-01

    The International Monitoring System (IMS) infrasound network is being deployed to monitor compliance with the Comprehensive Nuclear-Test-Ban Treaty (CTBT). Global-scale analyses of data recorded by this network indicate that the detection capability exhibits strong spatio-temporal variations. Previous studies estimated radiated acoustic source energy from remote infrasound observations using empirical yield-scaling relations, which account for the along-path stratospheric winds. Although the empirical wind correction reduces the variance in the explosive energy versus pressure relationship, large error remains in the yield estimates. Numerical modeling techniques are now widely employed to investigate the role of different factors describing atmospheric infrasound sources and propagation. Here we develop a theoretical attenuation relation from a large set of numerical simulations using the Parabolic Equation method. This relation accounts for the effects of the source frequency; geometrical spreading and dissipation; and realistic atmospheric specifications on the pressure wave attenuation. Compared with previous studies, the derived attenuation relation incorporates a more realistic physical description of infrasound propagation. By incorporating real ambient noise information at the receivers, we obtain the minimum detectable source amplitude in the frequency band of interest for detecting explosions. Empirical relations between the source spectrum and explosion yield are used to infer detection thresholds in tons of TNT equivalent. In the context of future verification of the CTBT, the obtained attenuation relation provides a more realistic picture of the spatio-temporal variability of the IMS network performance. The attenuation relation could also be used in the design and maintenance of an arbitrary infrasound monitoring network.

  11. A Generic Framework of Performance Measurement in Networked Enterprises

    NASA Astrophysics Data System (ADS)

    Kim, Duk-Hyun; Kim, Cheolhan

    Performance measurement (PM) is essential for managing networked enterprises (NEs) because it greatly affects the effectiveness of collaboration among members of NE.PM in NE requires somewhat different approaches from PM in a single enterprise because of heterogeneity, dynamism, and complexity of NE’s. This paper introduces a generic framework of PM in NE (we call it NEPM) based on the Balanced Scorecard (BSC) approach. In NEPM key performance indicators and cause-and-effect relationships among them are defined in a generic strategy map. NEPM could be applied to various types of NEs after specializing KPIs and relationships among them. Effectiveness of NEPM is shown through a case study of some Korean NEs.

  12. Evaluating the High Risk Groups for Suicide: A Comparison of Logistic Regression, Support Vector Machine, Decision Tree and Artificial Neural Network

    PubMed Central

    AMINI, Payam; AHMADINIA, Hasan; POOROLAJAL, Jalal; MOQADDASI AMIRI, Mohammad

    2016-01-01

    Background: We aimed to assess the high-risk group for suicide using different classification methods includinglogistic regression (LR), decision tree (DT), artificial neural network (ANN), and support vector machine (SVM). Methods: We used the dataset of a study conducted to predict risk factors of completed suicide in Hamadan Province, the west of Iran, in 2010. To evaluate the high-risk groups for suicide, LR, SVM, DT and ANN were performed. The applied methods were compared using sensitivity, specificity, positive predicted value, negative predicted value, accuracy and the area under curve. Cochran-Q test was implied to check differences in proportion among methods. To assess the association between the observed and predicted values, Ø coefficient, contingency coefficient, and Kendall tau-b were calculated. Results: Gender, age, and job were the most important risk factors for fatal suicide attempts in common for four methods. SVM method showed the highest accuracy 0.68 and 0.67 for training and testing sample, respectively. However, this method resulted in the highest specificity (0.67 for training and 0.68 for testing sample) and the highest sensitivity for training sample (0.85), but the lowest sensitivity for the testing sample (0.53). Cochran-Q test resulted in differences between proportions in different methods (P<0.001). The association of SVM predictions and observed values, Ø coefficient, contingency coefficient, and Kendall tau-b were 0.239, 0.232 and 0.239, respectively. Conclusion: SVM had the best performance to classify fatal suicide attempts comparing to DT, LR and ANN. PMID:27957463

  13. Support vector machine-an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river?

    PubMed

    Liu, Mei; Lu, Jun

    2014-09-01

    Water quality forecasting in agricultural drainage river basins is difficult because of the complicated nonpoint source (NPS) pollution transport processes and river self-purification processes involved in highly nonlinear problems. Artificial neural network (ANN) and support vector model (SVM) were developed to predict total nitrogen (TN) and total phosphorus (TP) concentrations for any location of the river polluted by agricultural NPS pollution in eastern China. River flow, water temperature, flow travel time, rainfall, dissolved oxygen, and upstream TN or TP concentrations were selected as initial inputs of the two models. Monthly, bimonthly, and trimonthly datasets were selected to train the two models, respectively, and the same monthly dataset which had not been used for training was chosen to test the models in order to compare their generalization performance. Trial and error analysis and genetic algorisms (GA) were employed to optimize the parameters of ANN and SVM models, respectively. The results indicated that the proposed SVM models performed better generalization ability due to avoiding the occurrence of overtraining and optimizing fewer parameters based on structural risk minimization (SRM) principle. Furthermore, both TN and TP SVM models trained by trimonthly datasets achieved greater forecasting accuracy than corresponding ANN models. Thus, SVM models will be a powerful alternative method because it is an efficient and economic tool to accurately predict water quality with low risk. The sensitivity analyses of two models indicated that decreasing upstream input concentrations during the dry season and NPS emission along the reach during average or flood season should be an effective way to improve Changle River water quality. If the necessary water quality and hydrology data and even trimonthly data are available, the SVM methodology developed here can easily be applied to other NPS-polluted rivers.

  14. The Deep Space Network: Noise temperature concepts, measurements, and performance

    NASA Technical Reports Server (NTRS)

    Stelzried, C. T.

    1982-01-01

    The use of higher operational frequencies is being investigated for improved performance of the Deep Space Network. Noise temperature and noise figure concepts are used to describe the noise performance of these receiving systems. The ultimate sensitivity of a linear receiving system is limited by the thermal noise of the source and the quantum noise of the receiver amplifier. The atmosphere, antenna and receiver amplifier of an Earth station receiving system are analyzed separately and as a system. Performance evaluation and error analysis techniques are investigated. System noise temperature and antenna gain parameters are combined to give an overall system figure of merit G/T. Radiometers are used to perform radio ""star'' antenna and system sensitivity calibrations. These are analyzed and the performance of several types compared to an idealized total power radiometer. The theory of radiative transfer is applicable to the analysis of transmission medium loss. A power series solution in terms of the transmission medium loss is given for the solution of the noise temperature contribution.

  15. Two-Dimensional Confined Jet Thrust Vector Control: Operating Mechanisms and Performance

    DTIC Science & Technology

    1989-03-01

    Avdilability Codes Nsf / AvduI and Ior 4 01st Specia Appr-ved for public release; distribution unlimited Pr ef ace In this thesis, I continued the...exceptionally high quality test articles, also with impossible deadlines. - the von Karman Institute, Dr. M. Carbonaro provided me with theoretical and...Schlieren photographs and video tapes were used to study flow separation and internal shock structures. Nozzle performance parameters were determined for

  16. An easily fabricated high performance ionic polymer based sensor network

    NASA Astrophysics Data System (ADS)

    Zhu, Zicai; Wang, Yanjie; Hu, Xiaopin; Sun, Xiaofei; Chang, Longfei; Lu, Pin

    2016-08-01

    Ionic polymer materials can generate an electrical potential from ion migration under an external force. For traditional ionic polymer metal composite sensors, the output voltage is very small (a few millivolts), and the fabrication process is complex and time-consuming. This letter presents an ionic polymer based network of pressure sensors which is easily and quickly constructed, and which can generate high voltage. A 3 × 3 sensor array was prepared by casting Nafion solution directly over copper wires. Under applied pressure, two different levels of voltage response were observed among the nine nodes in the array. For the group producing the higher level, peak voltages reached as high as 25 mV. Computational stress analysis revealed the physical origin of the different responses. High voltages resulting from the stress concentration and asymmetric structure can be further utilized to modify subsequent designs to improve the performance of similar sensors.

  17. Spiking neural networks on high performance computer clusters

    NASA Astrophysics Data System (ADS)

    Chen, Chong; Taha, Tarek M.

    2011-09-01

    In this paper we examine the acceleration of two spiking neural network models on three clusters of multicore processors representing three categories of processors: x86, STI Cell, and NVIDIA GPGPUs. The x86 cluster utilized consists of 352 dualcore AMD Opterons, the Cell cluster consists of 320 Sony Playstation 3s, while the GPGPU cluster contains 32 NVIDIA Tesla S1070 systems. The results indicate that the GPGPU platform can dominate in performance compared to the Cell and x86 platforms examined. From a cost perspective, the GPGPU is more expensive in terms of neuron/s throughput. If the cost of GPGPUs go down in the future, this platform will become very cost effective for these models.

  18. Assessing the performance of ultrafast vector flow imaging in the neonatal heart via multiphysics modeling and in-vitro experiments.

    PubMed

    Van Cauwenberge, Joris; Lovstakken, Lasse; Fadnes, Solveig; Rodriguez-Molares, Alfonso; Vierendeels, Jan; Segers, Patrick; Swillens, Abigail

    2016-08-01

    Ultrafast vector flow imaging would benefit newborn patients with congenital heart disorders, but still requires thorough validation before translation to clinical practice. This study investigates 2D speckle tracking of intraventricular blood flow in neonates when transmitting diverging waves at ultrafast frame rate. Computational and in-vitro studies enabled us to quantify the performance and identify artefacts related to the flow and the imaging sequence. First, synthetic ultrasound images of a neonate's left ventricular flow pattern were obtained with the ultrasound simulator Field II by propagating point scatterers according to 3D intraventricular flow fields obtained with computational fluid dynamics (CFD). Non-compounded diverging waves (opening angle of 60°) were transmitted at a pulse repetition frequency of 9 kHz. Speckle tracking of the B-mode data provided 2D flow estimates at 180 Hz, which were compared to the CFD flow field. We demonstrated that the diastolic inflow jet showed a strong bias in the lateral velocity estimates at the edges of the jet, as confirmed by additional in-vitro tests on a jet flow phantom. Further, speckle tracking performance was highly dependent on the cardiac phase with low flows (< 5 cm/s), high spatial flow gradients and out-of-plane flow as deteriorating factors. Despite the observed artefacts, a good overall performance of 2D speckle tracking was obtained with a median magnitude underestimation and angular deviation of respectively 28% and 13.5° during systole, and 16% and 10.5° during diastole.

  19. Assessing the Performance of Ultrafast Vector Flow Imaging in the Neonatal Heart via Multiphysics Modeling and In Vitro Experiments.

    PubMed

    Van Cauwenberge, Joris; Lovstakken, Lasse; Fadnes, Solveig; Rodriguez-Morales, Alfonso; Vierendeels, Jan; Segers, Patrick; Swillens, Abigail

    2016-11-01

    Ultrafast vector flow imaging would benefit newborn patients with congenital heart disorders, but still requires thorough validation before translation to clinical practice. This paper investigates 2-D speckle tracking (ST) of intraventricular blood flow in neonates when transmitting diverging waves at ultrafast frame rate. Computational and in vitro studies enabled us to quantify the performance and identify artifacts related to the flow and the imaging sequence. First, synthetic ultrasound images of a neonate's left ventricular flow pattern were obtained with the ultrasound simulator Field II by propagating point scatterers according to 3-D intraventricular flow fields obtained with computational fluid dynamics (CFD). Noncompounded diverging waves (opening angle of 60°) were transmitted at a pulse repetition frequency of 9 kHz. ST of the B-mode data provided 2-D flow estimates at 180 Hz, which were compared with the CFD flow field. We demonstrated that the diastolic inflow jet showed a strong bias in the lateral velocity estimates at the edges of the jet, as confirmed by additional in vitro tests on a jet flow phantom. Furthermore, ST performance was highly dependent on the cardiac phase with low flows (<5 cm/s), high spatial flow gradients, and out-of-plane flow as deteriorating factors. Despite the observed artifacts, a good overall performance of 2-D ST was obtained with a median magnitude underestimation and angular deviation of, respectively, 28% and 13.5° during systole and 16% and 10.5° during diastole.

  20. On-sky Performance Analysis of the Vector Apodizing Phase Plate Coronagraph on MagAO/Clio2

    NASA Astrophysics Data System (ADS)

    Otten, Gilles P. P. L.; Snik, Frans; Kenworthy, Matthew A.; Keller, Christoph U.; Males, Jared R.; Morzinski, Katie M.; Close, Laird M.; Codona, Johanan L.; Hinz, Philip M.; Hornburg, Kathryn J.; Brickson, Leandra L.; Escuti, Michael J.

    2017-01-01

    We report on the performance of a vector apodizing phase plate coronagraph that operates over a wavelength range of 2–5 μm and is installed in MagAO/Clio2 at the 6.5 m Magellan Clay telescope at Las Campanas Observatory, Chile. The coronagraph manipulates the phase in the pupil to produce three beams yielding two coronagraphic point-spread functions (PSFs) and one faint leakage PSF. The phase pattern is imposed through the inherently achromatic geometric phase, enabled by liquid crystal technology and polarization techniques. The coronagraphic optic is manufactured using a direct-write technique for precise control of the liquid crystal pattern and multitwist retarders for achromatization. By integrating a linear phase ramp to the coronagraphic phase pattern, two separated coronagraphic PSFs are created with a single pupil-plane optic, which makes it robust and easy to install in existing telescopes. The two coronagraphic PSFs contain a 180° dark hole on each side of a star, and these complementary copies of the star are used to correct the seeing halo close to the star. To characterize the coronagraph, we collected a data set of a bright (mL = 0–1) nearby star with ∼1.5 hr of observing time. By rotating and optimally scaling one PSF and subtracting it from the other PSF, we see a contrast improvement by 1.46 magnitudes at 3.5 λ /D. With regular angular differential imaging at 3.9 μm, the MagAO vector apodizing phase plate coronagraph delivers a 5σ {{Δ }}{mag} contrast of 8.3 (={10}-3.3) at 2 λ /D and 12.2 (={10}-4.8) at 3.5 λ /D.

  1. Social Networks Use, Loneliness and Academic Performance among University Students

    ERIC Educational Resources Information Center

    Stankovska, Gordana; Angelkovska, Slagana; Grncarovska, Svetlana Pandiloska

    2016-01-01

    The world is extensively changed by Social Networks Sites (SNSs) on the Internet. A large number of children and adolescents in the world have access to the internet and are exposed to the internet at a very early age. Most of them use the Social Networks Sites with the purpose of exchanging academic activities and developing a social network all…

  2. Direct Fabrication of 3D Metallic Networks and Their Performance.

    PubMed

    Ron, Racheli; Gachet, David; Rechav, Katya; Salomon, Adi

    2017-02-01

    Fabrication of macroscopic nanoporous metallic networks is challenging, because it demands fine structures at the nanoscale over a large-scale. A technique to form pure scalable networks is introduced. The networked-metals ("Netals") exhibit a strong interaction with light and indicate a large fraction of hot-electrons generation. These hot-electrons are available to derive photocatalytic processes.

  3. Analyzing dynamic performance of power systems over parameter space using the method of normal forms of vector fields

    NASA Astrophysics Data System (ADS)

    Zhu, Songzhe

    Today's power systems have become more and more stressed due to the high utilization of available facilities. The complex dynamic behavior of large stressed power systems following disturbances can not be fully explained with present tools, such as linear eigen-analysis tools and nonlinear time-domain simulation methods. This research work applies a nonlinear analytical tool, the method of normal forms of vector fields, to help understand the complex transient oscillations in stressed power systems. The method of normal forms is a well-known mathematical tool to study systems of differential equations. The basic idea is to simplify the dynamical system by a sequence of nonlinear coordinate transformations. If there is no resonance in the system, then the nonlinear vector field can be turned into a linear one by the transformations. Previous work applied the second-order normal form transformation under non-resonance condition to power system dynamical equations. The nonlinear interaction among the fundamental modes was investigated. Based on these efforts, this work extends the application of normal forms to evaluate the dynamic performance of power systems taking into account changing operation conditions. As the resonance and near-resonance could occur in parameter space, a new normal form transformation under second order resonance condition is derived. The analysis shows that the high nonlinearity resulting from the resonance and near-resonance among poorly damped oscillatory modes and control modes is detrimental to the system performance. An approach to determine the resonance and near-resonance regions in parameter space is developed. The modes contributing to the detrimental behavior associated with the near-resonance region are identified by a procedure based on certain modal interaction indices. The state variables showing detrimental behavior are then determined using nonlinear participation factors. The accuracy of the prediction is verified by

  4. Wireless Body Area Network (WBAN) design techniques and performance evaluation.

    PubMed

    Khan, Jamil Yusuf; Yuce, Mehmet R; Bulger, Garrick; Harding, Benjamin

    2012-06-01

    In recent years interest in the application of Wireless Body Area Network (WBAN) for patient monitoring applications has grown significantly. A WBAN can be used to develop patient monitoring systems which offer flexibility to medical staff and mobility to patients. Patients monitoring could involve a range of activities including data collection from various body sensors for storage and diagnosis, transmitting data to remote medical databases, and controlling medical appliances, etc. Also, WBANs could operate in an interconnected mode to enable remote patient monitoring using telehealth/e-health applications. A WBAN can also be used to monitor athletes' performance and assist them in training activities. For such applications it is very important that a WBAN collects and transmits data reliably, and in a timely manner to a monitoring entity. In order to address these issues, this paper presents WBAN design techniques for medical applications. We examine the WBAN design issues with particular emphasis on the design of MAC protocols and power consumption profiles of WBAN. Some simulation results are presented to further illustrate the performances of various WBAN design techniques.

  5. Performance Improvement in Geographic Routing for Vehicular Ad Hoc Networks

    PubMed Central

    Kaiwartya, Omprakash; Kumar, Sushil; Lobiyal, D. K.; Abdullah, Abdul Hanan; Hassan, Ahmed Nazar

    2014-01-01

    Geographic routing is one of the most investigated themes by researchers for reliable and efficient dissemination of information in Vehicular Ad Hoc Networks (VANETs). Recently, different Geographic Distance Routing (GEDIR) protocols have been suggested in the literature. These protocols focus on reducing the forwarding region towards destination to select the Next Hop Vehicles (NHV). Most of these protocols suffer from the problem of elevated one-hop link disconnection, high end-to-end delay and low throughput even at normal vehicle speed in high vehicle density environment. This paper proposes a Geographic Distance Routing protocol based on Segment vehicle, Link quality and Degree of connectivity (SLD-GEDIR). The protocol selects a reliable NHV using the criteria segment vehicles, one-hop link quality and degree of connectivity. The proposed protocol has been simulated in NS-2 and its performance has been compared with the state-of-the-art protocols: P-GEDIR, J-GEDIR and V-GEDIR. The empirical results clearly reveal that SLD-GEDIR has lower link disconnection and end-to-end delay, and higher throughput as compared to the state-of-the-art protocols. It should be noted that the performance of the proposed protocol is preserved irrespective of vehicle density and speed. PMID:25429415

  6. Performance improvement in geographic routing for Vehicular Ad Hoc Networks.

    PubMed

    Kaiwartya, Omprakash; Kumar, Sushil; Lobiyal, D K; Abdullah, Abdul Hanan; Hassan, Ahmed Nazar

    2014-11-25

    Geographic routing is one of the most investigated themes by researchers for reliable and efficient dissemination of information in Vehicular Ad Hoc Networks (VANETs). Recently, different Geographic Distance Routing (GEDIR) protocols have been suggested in the literature. These protocols focus on reducing the forwarding region towards destination to select the Next Hop Vehicles (NHV). Most of these protocols suffer from the problem of elevated one-hop link disconnection, high end-to-end delay and low throughput even at normal vehicle speed in high vehicle density environment. This paper proposes a Geographic Distance Routing protocol based on Segment vehicle, Link quality and Degree of connectivity (SLD-GEDIR). The protocol selects a reliable NHV using the criteria segment vehicles, one-hop link quality and degree of connectivity. The proposed protocol has been simulated in NS-2 and its performance has been compared with the state-of-the-art protocols: P-GEDIR, J-GEDIR and V-GEDIR. The empirical results clearly reveal that SLD-GEDIR has lower link disconnection and end-to-end delay, and higher throughput as compared to the state-of-the-art protocols. It should be noted that the performance of the proposed protocol is preserved irrespective of vehicle density and speed.

  7. Performance of a laser microsatellite network with an optical preamplifier.

    PubMed

    Arnon, Shlomi

    2005-04-01

    Laser satellite communication (LSC) uses free space as a propagation medium for various applications, such as intersatellite communication or satellite networking. An LSC system includes a laser transmitter and an optical receiver. For communication to occur, the line of sight of the transmitter and the receiver must be aligned. However, mechanical vibration and electronic noise in the control system reduce alignment between the transmitter laser beam and the receiver field of view (FOV), which results in pointing errors. The outcome of pointing errors is fading of the received signal, which leads to impaired link performance. An LSC system is considered in which the optical preamplifier is incorporated into the receiver, and a bit error probability (BEP) model is derived that takes into account the statistics of the pointing error as well as the optical amplifier and communication system parameters. The model and the numerical calculation results indicate that random pointing errors of sigma(chi)2G > 0.05 penalize communication performance dramatically for all combinations of optical amplifier gains and noise figures that were calculated.

  8. Clinical Performance of a New Biomimetic Double Network Material

    PubMed Central

    Dirxen, Christine; Blunck, Uwe; Preissner, Saskia

    2013-01-01

    Background: The development of ceramics during the last years was overwhelming. However, the focus was laid on the hardness and the strength of the restorative materials, resulting in high antagonistic tooth wear. This is critical for patients with bruxism. Objectives: The purpose of this study was to evaluate the clinical performance of the new double hybrid material for non-invasive treatment approaches. Material and Methods: The new approach of the material tested, was to modify ceramics to create a biomimetic material that has similar physical properties like dentin and enamel and is still as strong as conventional ceramics. Results: The produced crowns had a thickness ranging from 0.5 to 1.5 mm. To evaluate the clinical performance and durability of the crowns, the patient was examined half a year later. The crowns were still intact and soft tissues appeared healthy and this was achieved without any loss of tooth structure. Conclusions: The material can be milled to thin layers, but is still strong enough to prevent cracks which are stopped by the interpenetrating polymer within the network. Depending on the clinical situation, minimally- up to non-invasive restorations can be milled. Clinical Relevance: Dentistry aims in preservation of tooth structure. Patients suffering from loss of tooth structure (dental erosion, Amelogenesis imperfecta) or even young patients could benefit from minimally-invasive crowns. Due to a Vickers hardness between dentin and enamel, antagonistic tooth wear is very low. This might be interesting for treating patients with bruxism. PMID:24167534

  9. Performance of Wireless Unattended Sensor Network in Maritime Applications

    DTIC Science & Technology

    2007-06-01

    61 Figure 22. Hard Surface and Both “ON” Water Network Formation Times ....................62 Figure 23. One “ON”/One “IN” and Both “IN” Water ...Hard Surface Network Stability Trials Parent Switching................................65 Figure 26. Both “ON” the Water Network Stability Trials Parent...hard surface and both “ON” the water network formation times while Figure 23 depicts the one “ON”/one “IN” the water and both “IN” the water network formation

  10. Is functional integration of resting state brain networks an unspecific biomarker for working memory performance?

    PubMed

    Alavash, Mohsen; Doebler, Philipp; Holling, Heinz; Thiel, Christiane M; Gießing, Carsten

    2015-03-01

    Is there one optimal topology of functional brain networks at rest from which our cognitive performance would profit? Previous studies suggest that functional integration of resting state brain networks is an important biomarker for cognitive performance. However, it is still unknown whether higher network integration is an unspecific predictor for good cognitive performance or, alternatively, whether specific network organization during rest predicts only specific cognitive abilities. Here, we investigated the relationship between network integration at rest and cognitive performance using two tasks that measured different aspects of working memory; one task assessed visual-spatial and the other numerical working memory. Network clustering, modularity and efficiency were computed to capture network integration on different levels of network organization, and to statistically compare their correlations with the performance in each working memory test. The results revealed that each working memory aspect profits from a different resting state topology, and the tests showed significantly different correlations with each of the measures of network integration. While higher global network integration and modularity predicted significantly better performance in visual-spatial working memory, both measures showed no significant correlation with numerical working memory performance. In contrast, numerical working memory was superior in subjects with highly clustered brain networks, predominantly in the intraparietal sulcus, a core brain region of the working memory network. Our findings suggest that a specific balance between local and global functional integration of resting state brain networks facilitates special aspects of cognitive performance. In the context of working memory, while visual-spatial performance is facilitated by globally integrated functional resting state brain networks, numerical working memory profits from increased capacities for local processing

  11. Performance evaluation of NASA/KSC CAD/CAE graphics local area network

    NASA Technical Reports Server (NTRS)

    Zobrist, George

    1988-01-01

    This study had as an objective the performance evaluation of the existing CAD/CAE graphics network at NASA/KSC. This evaluation will also aid in projecting planned expansions, such as the Space Station project on the existing CAD/CAE network. The objectives were achieved by collecting packet traffic on the various integrated sub-networks. This included items, such as total number of packets on the various subnetworks, source/destination of packets, percent utilization of network capacity, peak traffic rates, and packet size distribution. The NASA/KSC LAN was stressed to determine the useable bandwidth of the Ethernet network and an average design station workload was used to project the increased traffic on the existing network and the planned T1 link. This performance evaluation of the network will aid the NASA/KSC network managers in planning for the integration of future workload requirements into the existing network.

  12. Battery Performance Modelling ad Simulation: a Neural Network Based Approach

    NASA Astrophysics Data System (ADS)

    Ottavianelli, Giuseppe; Donati, Alessandro

    2002-01-01

    This project has developed on the background of ongoing researches within the Control Technology Unit (TOS-OSC) of the Special Projects Division at the European Space Operations Centre (ESOC) of the European Space Agency. The purpose of this research is to develop and validate an Artificial Neural Network tool (ANN) able to model, simulate and predict the Cluster II battery system's performance degradation. (Cluster II mission is made of four spacecraft flying in tetrahedral formation and aimed to observe and study the interaction between sun and earth by passing in and out of our planet's magnetic field). This prototype tool, named BAPER and developed with a commercial neural network toolbox, could be used to support short and medium term mission planning in order to improve and maximise the batteries lifetime, determining which are the future best charge/discharge cycles for the batteries given their present states, in view of a Cluster II mission extension. This study focuses on the five Silver-Cadmium batteries onboard of Tango, the fourth Cluster II satellite, but time restrains have allowed so far to perform an assessment only on the first battery. In their most basic form, ANNs are hyper-dimensional curve fits for non-linear data. With their remarkable ability to derive meaning from complicated or imprecise history data, ANN can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. ANNs learn by example, and this is why they can be described as an inductive, or data-based models for the simulation of input/target mappings. A trained ANN can be thought of as an "expert" in the category of information it has been given to analyse, and this expert can then be used, as in this project, to provide projections given new situations of interest and answer "what if" questions. The most appropriate algorithm, in terms of training speed and memory storage requirements, is clearly the Levenberg

  13. A Bayesian Network Approach to Modeling Learning Progressions and Task Performance. CRESST Report 776

    ERIC Educational Resources Information Center

    West, Patti; Rutstein, Daisy Wise; Mislevy, Robert J.; Liu, Junhui; Choi, Younyoung; Levy, Roy; Crawford, Aaron; DiCerbo, Kristen E.; Chappel, Kristina; Behrens, John T.

    2010-01-01

    A major issue in the study of learning progressions (LPs) is linking student performance on assessment tasks to the progressions. This report describes the challenges faced in making this linkage using Bayesian networks to model LPs in the field of computer networking. The ideas are illustrated with exemplar Bayesian networks built on Cisco…

  14. Models of logistic regression analysis, support vector machine, and back-propagation neural network based on serum tumor markers in colorectal cancer diagnosis.

    PubMed

    Zhang, B; Liang, X L; Gao, H Y; Ye, L S; Wang, Y G

    2016-05-13

    We evaluated the application of three machine learning algorithms, including logistic regression, support vector machine and back-propagation neural network, for diagnosing congenital heart disease and colorectal cancer. By inspecting related serum tumor marker levels in colorectal cancer patients and healthy subjects, early diagnosis models for colorectal cancer were built using three machine learning algorithms to assess their corresponding diagnostic values. Except for serum alpha-fetoprotein, the levels of 11 other serum markers of patients in the colorectal cancer group were higher than those in the benign colorectal cancer group (P < 0.05). The results of logistic regression analysis indicted that individual detection of serum carcinoembryonic antigens, CA199, CA242, CA125, and CA153 and their combined detection was effective for diagnosing colorectal cancer. Combined detection had a better diagnostic effect with a sensitivity of 94.2% and specificity of 97.7%; combining serum carcinoembryonic antigens, CA199, CA242, CA125, and CA153, with the support vector machine diagnosis model and back-propagation, a neural network diagnosis model was built with diagnostic accuracies of 82 and 75%, sensitivities of 85 and 80%, and specificities of 80 and 70%, respectively. Colorectal cancer diagnosis models based on the three machine learning algorithms showed high diagnostic value and can help obtain evidence for the early diagnosis of colorectal cancer.

  15. The Current State of Human Performance Technology: A Citation Network Analysis of "Performance Improvement Quarterly," 1988-2010

    ERIC Educational Resources Information Center

    Cho, Yonjoo; Jo, Sung Jun; Park, Sunyoung; Kang, Ingu; Chen, Zengguan

    2011-01-01

    This study conducted a citation network analysis (CNA) of human performance technology (HPT) to examine its current state of the field. Previous reviews of the field have used traditional research methods, such as content analysis, survey, Delphi, and citation analysis. The distinctive features of CNA come from using a social network analysis…

  16. Support vector machine to predict diesel engine performance and emission parameters fueled with nano-particles additive to diesel fuel

    NASA Astrophysics Data System (ADS)

    Ghanbari, M.; Najafi, G.; Ghobadian, B.; Mamat, R.; Noor, M. M.; Moosavian, A.

    2015-12-01

    This paper studies the use of adaptive Support Vector Machine (SVM) to predict the performance parameters and exhaust emissions of a diesel engine operating on nanodiesel blended fuels. In order to predict the engine parameters, the whole experimental data were randomly divided into training and testing data. For SVM modelling, different values for radial basis function (RBF) kernel width and penalty parameters (C) were considered and the optimum values were then found. The results demonstrate that SVM is capable of predicting the diesel engine performance and emissions. In the experimental step, Carbon nano tubes (CNT) (40, 80 and 120 ppm) and nano silver particles (40, 80 and 120 ppm) with nanostructure were prepared and added as additive to the diesel fuel. Six cylinders, four-stroke diesel engine was fuelled with these new blended fuels and operated at different engine speeds. Experimental test results indicated the fact that adding nano particles to diesel fuel, increased diesel engine power and torque output. For nano-diesel it was found that the brake specific fuel consumption (bsfc) was decreased compared to the net diesel fuel. The results proved that with increase of nano particles concentrations (from 40 ppm to 120 ppm) in diesel fuel, CO2 emission increased. CO emission in diesel fuel with nano-particles was lower significantly compared to pure diesel fuel. UHC emission with silver nano-diesel blended fuel decreased while with fuels that contains CNT nano particles increased. The trend of NOx emission was inverse compared to the UHC emission. With adding nano particles to the blended fuels, NOx increased compared to the net diesel fuel. The tests revealed that silver & CNT nano particles can be used as additive in diesel fuel to improve complete combustion of the fuel and reduce the exhaust emissions significantly.

  17. 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.

  18. The Global Seismographic Network (GSN): Challenges and Methods for Maintaining High Quality Network Performance

    NASA Astrophysics Data System (ADS)

    Hafner, Katrin; Davis, Peter; Wilson, David; Sumy, Danielle; Woodward, Bob

    2016-04-01

    The Global Seismographic Network (GSN) is a 152 station, globally-distributed, permanent network of state-of-the-art seismological and geophysical sensors. The GSN has been operating for over 20 years via an ongoing successful partnership between IRIS, the USGS, the University of California at San Diego, NSF and numerous host institutions worldwide. The central design goal of the GSN may be summarized as "to record with full fidelity and bandwidth all seismic signals above the Earth noise, accompanied by some efforts to reduce Earth noise by deployment strategies". While many of the technical design goals have been met, we continue to strive for higher data quality with a combination of new sensors and improved installation techniques designed to achieve the lowest noise possible under existing site conditions. Data from the GSN are used not only for research, but on a daily basis as part of the operational missions of the USGS NEIC, NOAA tsunami warning centers, the Comprehensive Nuclear-Test-Ban-Treaty Organization as well as other organizations. In the recent period of very tight funding budgets, the primary challenges for the GSN include maintaining these operational capabilities while simultaneously developing and replacing the primary sensors, maintaining high quality data and repairing station infrastructure. Aging of GSN equipment and station infrastructure has resulted in renewed emphasis on developing, evaluating and implementing quality control tools such as MUSTANG and DQA to maintain the high data quality from the GSN stations. These tools allow the network operators to routinely monitor and analyze waveform data to detect and track problems and develop action plans as issues are found. We will present summary data quality metrics for the GSN as obtained via these quality assurance tools. In recent years, the GSN has standardized dataloggers to the Quanterra Q330HR data acquisition system at all but three stations resulting in significantly improved

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

    DTIC Science & Technology

    2012-08-01

    range of backoff misbehaviors on network performance in CSMA/CA-based wireless networks. 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17...Layer Misbeha- vior in Wireless Networks,” ACM Trans. Information and Systems Security , vol. 11, no. 4, pp. 19:1-19:28, July 2008. [10] S. Choi, K...Park, and C. kwon Kim, “On the Performance Characteristics of WLANs : Revisited,” Proc. ACM SIGMETRICS Int’l Conf. Measurement and Modeling of Computer

  20. Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

    PubMed Central

    2011-01-01

    Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed

  1. Performance of Wireless Networks Subject to Constraints and Failures

    DTIC Science & Technology

    2008-01-01

    in the Graduate College of the University of Illinois at Urbana-Champaign, 2008 Urbana, Illinois Doctoral Committee: Professor Nitin H. Vaidya, Chair...to my advisor Prof. Nitin Vaidya. As his student, I have had the freedom to seek my trajectory, while always having access to his advice. My frequent...computing and networking, pages 216–230. ACM Press, 2004. [5] Vartika Bhandari and Nitin H. Vaidya. On reliable broadcast in a radio network. In PODC ’05

  2. Performance optimisation through EPT-WBC in mobile ad hoc networks

    NASA Astrophysics Data System (ADS)

    Agarwal, Ratish; Gupta, Roopam; Motwani, Mahesh

    2016-03-01

    Mobile ad hoc networks are self-organised, infrastructure-less networks in which each mobile host works as a router to provide connectivity within the network. Nodes out of reach to each other can communicate with the help of intermediate routers (nodes). Routing protocols are the rules which determine the way in which these routing activities are to be performed. In cluster-based architecture, some selected nodes (clusterheads) are identified to bear the extra burden of network activities like routing. Selection of clusterheads is a critical issue which significantly affects the performance of the network. This paper proposes an enhanced performance and trusted weight-based clustering approach in which a number of performance factors such as trust, load balancing, energy consumption, mobility and battery power are considered for the selection of clusterheads. Moreover, the performance of the proposed scheme is compared with other existing approaches to demonstrate the effectiveness of the work.

  3. Performance analysis of an iSCSI-based unified storage network.

    PubMed

    Fu, Xiang-lin; Zhang, Kun; Xie, Chang-sheng

    2004-01-01

    In this paper, we introduced a novel storage architecture "Unified Storage Network", which merges NAC(Network Attached Channel) and SAN(Storage Area Network), and provides the file I/O services as NAS devices and provides the block I/O services as SAN. To overcome the drawbacks from FC, we employ iSCSI to implement the USN(Unified Storage Network). To evaluate whether iSCSI is more suitable for implementing the USN, we analyze iSCSI protocol and compare it with FC protocol from several components of a network protocol which impact the performance of the network. From the analysis and comparison, we can conclude that the iSCSI is more suitable for implementing the storage network than the FC under condition of the wide-area network. At last, we designed two groups of experiments carefully.

  4. Performance Evolution of IEEE 802.11b Wireless Local Area Network

    NASA Astrophysics Data System (ADS)

    Malik, Deepak; Singhal, Ankur

    2011-12-01

    The Wireless network can be employed to connect wired network to the wireless network. Wireless local area networks (WLAN) are more bandwidth limited as compared to the wired networks because they rely on an inexpensive, but error prone, physical medium (air). Hence it is important to evaluate their performance. This paper presents a study of IEEE 802.11b wireless LAN (WLAN). The performance evaluation has been presented via a series of test with different parameters such as data rate, different number of nodes and physical characteristics. The different qualities of service parameter are chosen to be throughput, media access delay and dropped data packets. The simulation results show that an IEEE 802.11b WLAN can support up to 60 clients with modest throughput. Finally the results are compared to evaluate the performance of wireless local networks.

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

    SciTech Connect

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

    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 cost 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.

  6. DISCRETE EVENT SIMULATION OF OPTICAL SWITCH MATRIX PERFORMANCE IN COMPUTER NETWORKS

    SciTech Connect

    Imam, Neena; Poole, Stephen W

    2013-01-01

    In this paper, we present application of a Discrete Event Simulator (DES) for performance modeling of optical switching devices in computer networks. Network simulators are valuable tools in situations where one cannot investigate the system directly. This situation may arise if the system under study does not exist yet or the cost of studying the system directly is prohibitive. Most available network simulators are based on the paradigm of discrete-event-based simulation. As computer networks become increasingly larger and more complex, sophisticated DES tool chains have become available for both commercial and academic research. Some well-known simulators are NS2, NS3, OPNET, and OMNEST. For this research, we have applied OMNEST for the purpose of simulating multi-wavelength performance of optical switch matrices in computer interconnection networks. Our results suggest that the application of DES to computer interconnection networks provides valuable insight in device performance and aids in topology and system optimization.

  7. 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.

  8. Impact of Network Activity Levels on the Performance of Passive Network Service Dependency Discovery

    SciTech Connect

    Carroll, Thomas E.; Chikkagoudar, Satish; Arthur-Durett, Kristine M.

    2015-11-02

    Network services often do not operate alone, but instead, depend on other services distributed throughout a network to correctly function. If a service fails, is disrupted, or degraded, it is likely to impair other services. The web of dependencies can be surprisingly complex---especially within a large enterprise network---and evolve with time. Acquiring, maintaining, and understanding dependency knowledge is critical for many network management and cyber defense activities. While automation can improve situation awareness for network operators and cyber practitioners, poor detection accuracy reduces their confidence and can complicate their roles. In this paper we rigorously study the effects of network activity levels on the detection accuracy of passive network-based service dependency discovery methods. The accuracy of all except for one method was inversely proportional to network activity levels. Our proposed cross correlation method was particularly robust to the influence of network activity. The proposed experimental treatment will further advance a more scientific evaluation of methods and provide the ability to determine their operational boundaries.

  9. Image Coding Based on Address Vector Quantization.

    NASA Astrophysics Data System (ADS)

    Feng, Yushu

    Image coding is finding increased application in teleconferencing, archiving, and remote sensing. This thesis investigates the potential of Vector Quantization (VQ), a relatively new source coding technique, for compression of monochromatic and color images. Extensions of the Vector Quantization technique to the Address Vector Quantization method have been investigated. In Vector Quantization, the image data to be encoded are first processed to yield a set of vectors. A codeword from the codebook which best matches the input image vector is then selected. Compression is achieved by replacing the image vector with the index of the code-word which produced the best match, the index is sent to the channel. Reconstruction of the image is done by using a table lookup technique, where the label is simply used as an address for a table containing the representative vectors. A code-book of representative vectors (codewords) is generated using an iterative clustering algorithm such as K-means, or the generalized Lloyd algorithm. A review of different Vector Quantization techniques are given in chapter 1. Chapter 2 gives an overview of codebook design methods including the Kohonen neural network to design codebook. During the encoding process, the correlation of the address is considered and Address Vector Quantization is developed for color image and monochrome image coding. Address VQ which includes static and dynamic processes is introduced in chapter 3. In order to overcome the problems in Hierarchical VQ, Multi-layer Address Vector Quantization is proposed in chapter 4. This approach gives the same performance as that of the normal VQ scheme but the bit rate is about 1/2 to 1/3 as that of the normal VQ method. In chapter 5, a Dynamic Finite State VQ based on a probability transition matrix to select the best subcodebook to encode the image is developed. In chapter 6, a new adaptive vector quantization scheme, suitable for color video coding, called "A Self -Organizing

  10. Issues in performing a network meta-analysis.

    PubMed

    Senn, Stephen; Gavini, Francois; Magrez, David; Scheen, André

    2013-04-01

    The example of the analysis of a collection of trials in diabetes consisting of a sparsely connected network of 10 treatments is used to make some points about approaches to analysis. In particular various graphical and tabular presentations, both of the network and of the results are provided and the connection to the literature of incomplete blocks is made. It is clear from this example that is inappropriate to treat the main effect of trial as random and the implications of this for analysis are discussed. It is also argued that the generalisation from a classic random-effect meta-analysis to one applied to a network usually involves strong assumptions about the variance components involved. Despite this, it is concluded that such an analysis can be a useful way of exploring a set of trials.

  11. Performance evaluation of a holographic optical neural network system

    NASA Astrophysics Data System (ADS)

    Lu, Thomas T.; Kostrzewski, Andrew A.; Chou, Hung; Wu, Shudong; Lin, Freddie S.

    1993-02-01

    One of the most outstanding properties of artificial neural networks is their capability for massive interconnection and parallel processing. Recently, specialized electronic neural network processors and VLSI neural chips have been introduced to the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections number with one-dimensional (1-D) electronic wires. High resolution pattern recognition problems may require a large number of neurons for parallel processing of the image. The holographic optical neural network (HONN) based on high resolution volume holographic materials is capable of providing 3-D massive parallel interconnection of tens of thousand of neurons. A HONN with 3600 neurons, contained in a portable briefcase, has been developed. Rotation-shift-scale invariant pattern recognition operations have been demonstrated with this system. System parameters, such as signal-to-noise ratio, dynamic range, and processing speed, will be discussed.

  12. Performance Impacts of Lower-Layer Cryptographic Methods in Mobile Wireless Ad Hoc Networks

    SciTech Connect

    VAN LEEUWEN, BRIAN P.; TORGERSON, MARK D.

    2002-10-01

    In high consequence systems, all layers of the protocol stack need security features. If network and data-link layer control messages are not secured, a network may be open to adversarial manipulation. The open nature of the wireless channel makes mobile wireless mobile ad hoc networks (MANETs) especially vulnerable to control plane manipulation. The objective of this research is to investigate MANET performance issues when cryptographic processing delays are applied at the data-link layer. The results of analysis are combined with modeling and simulation experiments to show that network performance in MANETs is highly sensitive to the cryptographic overhead.

  13. International network for capacity building for the control of emerging viral vector-borne zoonotic diseases: ARBO-ZOONET.

    PubMed

    Ahmed, J; Bouloy, M; Ergonul, O; Fooks, Ar; Paweska, J; Chevalier, V; Drosten, C; Moormann, R; Tordo, N; Vatansever, Z; Calistri, P; Estrada-Pena, A; Mirazimi, A; Unger, H; Yin, H; Seitzer, U

    2009-03-26

    Arboviruses are arthropod-borne viruses, which include West Nile fever virus (WNFV), a mosquito-borne virus, Rift Valley fever virus (RVFV), a mosquito-borne virus, and Crimean-Congo haemorrhagic fever virus (CCHFV), a tick-borne virus. These arthropod-borne viruses can cause disease in different domestic and wild animals and in humans, posing a threat to public health because of their epidemic and zoonotic potential. In recent decades, the geographical distribution of these diseases has expanded. Outbreaks of WNF have already occurred in Europe, especially in the Mediterranean basin. Moreover, CCHF is endemic in many European countries and serious outbreaks have occurred, particularly in the Balkans, Turkey and Southern Federal Districts of Russia. In 2000, RVF was reported for the first time outside the African continent, with cases being confirmed in Saudi Arabia and Yemen. This spread was probably caused by ruminant trade and highlights that there is a threat of expansion of the virus into other parts of Asia and Europe. In the light of global warming and globalisation of trade and travel, public interest in emerging zoonotic diseases has increased. This is especially evident regarding the geographical spread of vector-borne diseases. A multi-disciplinary approach is now imperative, and groups need to collaborate in an integrated manner that includes vector control, vaccination programmes, improved therapy strategies, diagnostic tools and surveillance, public awareness, capacity building and improvement of infrastructure in endemic regions.

  14. Measurements-based performance evaluation of 3G wireless networks supporting m-health services

    NASA Astrophysics Data System (ADS)

    Wac, Katarzyna E.; Bults, Richard; van Halteren, Aart; Konstantas, Dimitri; Nicola, Victor F.

    2004-12-01

    The emergence of 3G networks gives rise to new mobile services in many different areas of our daily life. Examples of demanding mobile services are mobile-healthcare (i.e. m-health) services allowing the continuous monitoring of a patient"s vital signs. However, a prerequisite for the successful deployment of m-health services are appropriate performance characteristics of transport services offered by an underlying wireless network (e.g. 3G). In this direction, the EU MobiHealth project targeted the evaluation of 3G networks and their ability to support demanding m-health services. The project developed and trialled a patient monitoring system, evaluating at the same time the network"s performance. This paper presents measurements based performance evaluation methodology developed and applied to assess network performance from an end-user perspective. In addition, it presents the (selected) speed-related evaluation (best-case scenario) results collected during the project. Our measurements show the dynamicity in the performance of 3G networks and phenomena negatively influencing this performance. Based on the evaluation results, we conclude that in-spite of certain shortcomings of existing 3G networks, they are suitable to support a significant set of m-health services. A set of recommendations provide a road map for both operators and service developers for design and deployment of m-health services.

  15. Statistical modelling of networked human-automation performance using working memory capacity.

    PubMed

    Ahmed, Nisar; de Visser, Ewart; Shaw, Tyler; Mohamed-Ameen, Amira; Campbell, Mark; Parasuraman, Raja

    2014-01-01

    This study examines the challenging problem of modelling the interaction between individual attentional limitations and decision-making performance in networked human-automation system tasks. Analysis of real experimental data from a task involving networked supervision of multiple unmanned aerial vehicles by human participants shows that both task load and network message quality affect performance, but that these effects are modulated by individual differences in working memory (WM) capacity. These insights were used to assess three statistical approaches for modelling and making predictions with real experimental networked supervisory performance data: classical linear regression, non-parametric Gaussian processes and probabilistic Bayesian networks. It is shown that each of these approaches can help designers of networked human-automated systems cope with various uncertainties in order to accommodate future users by linking expected operating conditions and performance from real experimental data to observable cognitive traits like WM capacity. Practitioner Summary: Working memory (WM) capacity helps account for inter-individual variability in operator performance in networked unmanned aerial vehicle supervisory tasks. This is useful for reliable performance prediction near experimental conditions via linear models; robust statistical prediction beyond experimental conditions via Gaussian process models and probabilistic inference about unknown task conditions/WM capacities via Bayesian network models.

  16. Blocking performance approximation in flexi-grid networks

    NASA Astrophysics Data System (ADS)

    Ge, Fei; Tan, Liansheng

    2016-12-01

    The blocking probability to the path requests is an important issue in flexible bandwidth optical communications. In this paper, we propose a blocking probability approximation method of path requests in flexi-grid networks. It models the bundled neighboring carrier allocation with a group of birth-death processes and provides a theoretical analysis to the blocking probability under variable bandwidth traffic. The numerical results show the effect of traffic parameters to the blocking probability of path requests. We use the first fit algorithm in network nodes to allocate neighboring carriers to path requests in simulations, and verify approximation results.

  17. An Implementation of Traffic Monitoring for UNIX Network Performance Management

    DTIC Science & Technology

    1993-03-01

    plttsr->network-node~network-node~plttsr); new_node,_recjpltlsr->trafflc_info nr-rewremc..plttsr, * new-node_rec~plutsr->next;=NULL; if (head- nodej -ec...plttsr); free(new nodejrec~plttsr); free(cur _node__rec..plttsr); displayjlong-term-statisticsý-report(head_node-rec-dltsr.tail- nodej -ec~dltsr) long~ern...NULL) head- nodej - ec..pltutrrnew node-rec-plttrrn tail-node-re4cplttri-new node-rec-plttrr else tail-node-rec-plttr->next=new node_rec-plttrrn 265

  18. Static and transient performance prediction for CFB boilers using a Bayesian-Gaussian Neural Network

    NASA Astrophysics Data System (ADS)

    Ye, Haiwen; Ni, Weidou

    1997-06-01

    A Bayesian-Gaussian Neural Network (BGNN) is put forward in this paper to predict the static and transient performance of Circulating Fluidized Bed (CFB) boilers. The advantages of this network over Back-Propagation Neural Networks (BPNNs), easier determination of topology, simpler and time saving in training process as well as self-organizing ability, make this network more practical in on-line performance prediction for complicated processes. Simulation shows that this network is comparable to the BPNNs in predicting the performance of CFB boilers. Good and practical on-line performance predictions are essential for operation guide and model predictive control of CFB boilers, which are under research by the authors.

  19. Visualizing weighted networks: a performance comparison of adjacency matrices versus node-link diagrams

    NASA Astrophysics Data System (ADS)

    McIntire, John P.; Osesina, O. Isaac; Bartley, Cecilia; Tudoreanu, M. Eduard; Havig, Paul R.; Geiselman, Eric E.

    2012-06-01

    Ensuring the proper and effective ways to visualize network data is important for many areas of academia, applied sciences, the military, and the public. Fields such as social network analysis, genetics, biochemistry, intelligence, cybersecurity, neural network modeling, transit systems, communications, etc. often deal with large, complex network datasets that can be difficult to interact with, study, and use. There have been surprisingly few human factors performance studies on the relative effectiveness of different graph drawings or network diagram techniques to convey information to a viewer. This is particularly true for weighted networks which include the strength of connections between nodes, not just information about which nodes are linked to other nodes. We describe a human factors study in which participants performed four separate network analysis tasks (finding a direct link between given nodes, finding an interconnected node between given nodes, estimating link strengths, and estimating the most densely interconnected nodes) on two different network visualizations: an adjacency matrix with a heat-map versus a node-link diagram. The results should help shed light on effective methods of visualizing network data for some representative analysis tasks, with the ultimate goal of improving usability and performance for viewers of network data displays.

  20. Vector network analyzer measurement of the amplitude of an electrically excited surface acoustic wave and validation by X-ray diffraction

    NASA Astrophysics Data System (ADS)

    Camara, I. S.; Croset, B.; Largeau, L.; Rovillain, P.; Thevenard, L.; Duquesne, J.-Y.

    2017-01-01

    Surface acoustic waves are used in magnetism to initiate magnetization switching, in microfluidics to control fluids and particles in lab-on-a-chip devices, and in quantum systems like two-dimensional electron gases, quantum dots, photonic cavities, and single carrier transport systems. For all these applications, an easy tool is highly needed to measure precisely the acoustic wave amplitude in order to understand the underlying physics and/or to optimize the device used to generate the acoustic waves. We present here a method to determine experimentally the amplitude of surface acoustic waves propagating on Gallium Arsenide generated by an interdigitated transducer. It relies on Vector Network Analyzer measurements of S parameters and modeling using the Coupling-Of-Modes theory. The displacements obtained are in excellent agreement with those measured by a very different method based on X-ray diffraction measurements.

  1. Distinguishing Parkinson's disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks.

    PubMed

    Segovia, Fermín; Illán, Ignacio A; Górriz, Juan M; Ramírez, Javier; Rominger, Axel; Levin, Johannes

    2015-01-01

    Differentiating between Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) is still a challenge, specially at early stages when the patients show similar symptoms. During last years, several computer systems have been proposed in order to improve the diagnosis of PD, but their accuracy is still limited. In this work we demonstrate a full automatic computer system to assist the diagnosis of PD using (18)F-DMFP PET data. First, a few regions of interest are selected by means of a two-sample t-test. The accuracy of the selected regions to separate PD from APS patients is then computed using a support vector machine classifier. The accuracy values are finally used to train a Bayesian network that can be used to predict the class of new unseen data. This methodology was evaluated using a database with 87 neuroimages, achieving accuracy rates over 78%. A fair comparison with other similar approaches is also provided.

  2. Algorithms for Performance, Dependability, and Performability Evaluation using Stochastic Activity Networks

    NASA Technical Reports Server (NTRS)

    Deavours, Daniel D.; Qureshi, M. Akber; Sanders, William H.

    1997-01-01

    Modeling tools and technologies are important for aerospace development. At the University of Illinois, we have worked on advancing the state of the art in modeling by Markov reward models in two important areas: reducing the memory necessary to numerically solve systems represented as stochastic activity networks and other stochastic Petri net extensions while still obtaining solutions in a reasonable amount of time, and finding numerically stable and memory-efficient methods to solve for the reward accumulated during a finite mission time. A long standing problem when modeling with high level formalisms such as stochastic activity networks is the so-called state space explosion, where the number of states increases exponentially with size of the high level model. Thus, the corresponding Markov model becomes prohibitively large and solution is constrained by the the size of primary memory. To reduce the memory necessary to numerically solve complex systems, we propose new methods that can tolerate such large state spaces that do not require any special structure in the model (as many other techniques do). First, we develop methods that generate row and columns of the state transition-rate-matrix on-the-fly, eliminating the need to explicitly store the matrix at all. Next, we introduce a new iterative solution method, called modified adaptive Gauss-Seidel, that exhibits locality in its use of data from the state transition-rate-matrix, permitting us to cache portions of the matrix and hence reduce the solution time. Finally, we develop a new memory and computationally efficient technique for Gauss-Seidel based solvers that avoids the need for generating rows of A in order to solve Ax = b. This is a significant performance improvement for on-the-fly methods as well as other recent solution techniques based on Kronecker operators. Taken together, these new results show that one can solve very large models without any special structure.

  3. Support vector regression model of wastewater bioreactor performance using microbial community diversity indices: effect of stress and bioaugmentation.

    PubMed

    Seshan, Hari; Goyal, Manish K; Falk, Michael W; Wuertz, Stefan

    2014-04-15

    The relationship between microbial community structure and function has been examined in detail in natural and engineered environments, but little work has been done on using microbial community information to predict function. We processed microbial community and operational data from controlled experiments with bench-scale bioreactor systems to predict reactor process performance. Four membrane-operated sequencing batch reactors treating synthetic wastewater were operated in two experiments to test the effects of (i) the toxic compound 3-chloroaniline (3-CA) and (ii) bioaugmentation targeting 3-CA degradation, on the sludge microbial community in the reactors. In the first experiment, two reactors were treated with 3-CA and two reactors were operated as controls without 3-CA input. In the second experiment, all four reactors were additionally bioaugmented with a Pseudomonas putida strain carrying a plasmid with a portion of the pathway for 3-CA degradation. Molecular data were generated from terminal restriction fragment length polymorphism (T-RFLP) analysis targeting the 16S rRNA and amoA genes from the sludge community. The electropherograms resulting from these T-RFs were used to calculate diversity indices - community richness, dynamics and evenness - for the domain Bacteria as well as for ammonia-oxidizing bacteria in each reactor over time. These diversity indices were then used to train and test a support vector regression (SVR) model to predict reactor performance based on input microbial community indices and operational data. Considering the diversity indices over time and across replicate reactors as discrete values, it was found that, although bioaugmentation with a bacterial strain harboring a subset of genes involved in the degradation of 3-CA did not bring about 3-CA degradation, it significantly affected the community as measured through all three diversity indices in both the general bacterial community and the ammonia-oxidizer community (

  4. Improving Stochastic Communication Network Performance: Reliability vs. Throughput

    DTIC Science & Technology

    1991-12-01

    ap- proach was only successful in computing a relaibility value for Network A. and even then, required on the order of hours to compute. The factoring...Algorithm for Sum of Disjoint Products,"IEEE Transactions on Relaibility Vol. R-36, No. 4: 445-453 (October 1987). 18. Page, L. B. and J. E. Perry

  5. Social Networks and Performance in Distributed Learning Communities

    ERIC Educational Resources Information Center

    Cadima, Rita; Ojeda, Jordi; Monguet, Josep M.

    2012-01-01

    Social networks play an essential role in learning environments as a key channel for knowledge sharing and students' support. In distributed learning communities, knowledge sharing does not occur as spontaneously as when a working group shares the same physical space; knowledge sharing depends even more on student informal connections. In this…

  6. Network based high performance concurrent computing. Progress report, [FY 1991

    SciTech Connect

    Sunderam, V.S.

    1991-12-31

    The overall objectives of this project are to investigate research issues pertaining to programming tools and efficiency issues in network based concurrent computing systems. The basis for these efforts is the PVM project that evolved during my visits to Oak Ridge Laboratories under the DOE Faculty Research Participation program; I continue to collaborate with researchers at Oak Ridge on some portions of the project.

  7. Performance Evaluation and Control of Distributed Computer Communication Networks.

    DTIC Science & Technology

    1985-09-01

    Pazos-Rangel "Bandwidth Allocation and Routing in ISDN’s," IEEE Communications Magazine , February 1984. Abstract The goal of communications network design...December 1982. [28] M. Gerla and R. Pazos, "Bandwidth Allocation and Routing in ISDN’s," IEEE Communications Magazine , February 1984. [29] R. Pazos

  8. Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs

    SciTech Connect

    Buitrago, Jaime; Asfour, Shihab

    2017-01-01

    Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. Finally, the New England electrical load data are used to train and validate the forecast prediction.

  9. Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs

    DOE PAGES

    Buitrago, Jaime; Asfour, Shihab

    2017-01-01

    Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input.more » Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. Finally, the New England electrical load data are used to train and validate the forecast prediction.« less

  10. 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.

  11. 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.

  12. A high-performance feedback neural network for solving convex nonlinear programming problems.

    PubMed

    Leung, Yee; Chen, Kai-Zhou; Gao, Xing-Bao

    2003-01-01

    Based on a new idea of successive approximation, this paper proposes a high-performance feedback neural network model for solving convex nonlinear programming problems. Differing from existing neural network optimization models, no dual variables, penalty parameters, or Lagrange multipliers are involved in the proposed network. It has the least number of state variables and is very simple in structure. In particular, the proposed network has better asymptotic stability. For an arbitrarily given initial point, the trajectory of the network converges to an optimal solution of the convex nonlinear programming problem under no more than the standard assumptions. In addition, the network can also solve linear programming and convex quadratic programming problems, and the new idea of a feedback network may be used to solve other optimization problems. Feasibility and efficiency are also substantiated by simulation examples.

  13. Moving Large Data Sets Over High-Performance Long Distance Networks

    SciTech Connect

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

    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 the 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.

  14. Support vector machines

    NASA Technical Reports Server (NTRS)

    Garay, Michael J.; Mazzoni, Dominic; Davies, Roger; Wagstaff, Kiri

    2004-01-01

    Support Vector Machines (SVMs) are a type of supervised learning algorith,, other examples of which are Artificial Neural Networks (ANNs), Decision Trees, and Naive Bayesian Classifiers. Supervised learning algorithms are used to classify objects labled by a 'supervisor' - typically a human 'expert.'.

  15. Singular Vectors' Subtle Secrets

    ERIC Educational Resources Information Center

    James, David; Lachance, Michael; Remski, Joan

    2011-01-01

    Social scientists use adjacency tables to discover influence networks within and among groups. Building on work by Moler and Morrison, we use ordered pairs from the components of the first and second singular vectors of adjacency matrices as tools to distinguish these groups and to identify particularly strong or weak individuals.

  16. Spectral Graph Theory Analysis of Software-Defined Networks to Improve Performance and Security

    DTIC Science & Technology

    2015-09-01

    networks for transmission operations in smart grids,” in the Proc. IEEE PES Innovative Smart Grid Technologies (ISGT), Washington, DC, 2013. [34] D...GRAPH THEORY ANALYSIS OF SOFTWARE-DEFINED NETWORKS TO IMPROVE PERFORMANCE AND SECURITY by Thomas C. Parker September 2015 Dissertation Co...September 2015 3. REPORT TYPE AND DATES COVERED Dissertation 4. TITLE AND SUBTITLE SPECTRAL GRAPH THEORY ANALYSIS OF SOFTWARE-DEFINED NETWORKS

  17. Communication, opponents, and clan performance in online games: a social network approach.

    PubMed

    Lee, Hong Joo; Choi, Jaewon; Kim, Jong Woo; Park, Sung Joo; Gloor, Peter

    2013-12-01

    Online gamers form clans voluntarily to play together and to discuss their real and virtual lives. Although these clans have diverse goals, they seek to increase their rank in the game community by winning more battles. Communications among clan members and battles with other clans may influence the performance of a clan. In this study, we compared the effects of communication structure inside a clan, and battle networks among clans, with the performance of the clans. We collected battle histories, posts, and comments on clan pages from a Korean online game, and measured social network indices for communication and battle networks. Communication structures in terms of density and group degree centralization index had no significant association with clan performance. However, the centrality of clans in the battle network was positively related to the performance of the clan. If a clan had many battle opponents, the performance of the clan improved.

  18. 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.

  19. 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.

  20. Measurements-based performance evaluation of 3G wireless networks supporting m-health services

    NASA Astrophysics Data System (ADS)

    Wac, Katarzyna E.; Bults, Richard; van Halteren, Aart; Konstantas, Dimitri; Nicola, Victor F.

    2005-01-01

    The emergence of 3G networks gives rise to new mobile services in many different areas of our daily life. Examples of demanding mobile services are mobile-healthcare (i.e. m-health) services allowing the continuous monitoring of a patient"s vital signs. However, a prerequisite for the successful deployment of m-health services are appropriate performance characteristics of transport services offered by an underlying wireless network (e.g. 3G). In this direction, the EU MobiHealth project targeted the evaluation of 3G networks and their ability to support demanding m-health services. The project developed and trialled a patient monitoring system, evaluating at the same time the network's performance. This paper presents measurements based performance evaluation methodology developed and applied to assess network performance from an end-user perspective. In addition, it presents the (selected) speed-related evaluation (best-case scenario) results collected during the project. Our measurements show the dynamicity in the performance of 3G networks and phenomena negatively influencing this performance. Based on the evaluation results, we conclude that in-spite of certain shortcomings of existing 3G networks, they are suitable to support a significant set of m-health services. A set of recommendations provide a road map for both operators and service developers for design and deployment of m-health services.

  1. Performance analysis of Integrated Communication and Control System networks

    NASA Technical Reports Server (NTRS)

    Halevi, Y.; Ray, A.

    1990-01-01

    This paper presents statistical analysis of delays in Integrated Communication and Control System (ICCS) networks that are based on asynchronous time-division multiplexing. The models are obtained in closed form for analyzing control systems with randomly varying delays. The results of this research are applicable to ICCS design for complex dynamical processes like advanced aircraft and spacecraft, autonomous manufacturing plants, and chemical and processing plants.

  2. Communications Performance of an Undersea Acoustic Wide-Area Network

    DTIC Science & Technology

    2006-03-01

    Seaweb technology . PEO IWS sponsored the Seaweb 2004 experiment analyzed in my thesis. The SSC San Diego Fellowship Program sponsored my research...layer protocol requiring the undersea vehicle to initiate all communications. As Seaweb advances technologically , the ability to maintain network-layer...racom buoys used in the Seaweb 2004 experiment incorporate FreeWave radio technology as well as Iridium satellite communication technology . The

  3. Physical Attractiveness, Social Network Location, and Performance in the Military

    DTIC Science & Technology

    2008-03-01

    Brass, 2001; Moore, 2006), as well as demographic variables, such as gender (Combs, 2003; Ibarra , 1992; Mulford, Orbell, Shatto, & Stockard, 1998), race...Combs, 2003), and education ( Ibarra , 1992), to network location. Generally, these findings have indicated that individual differences contribute...Balkundi & Harrison, 2006; Ibarra , 1993) and attractiveness (Borgatti, 2006c; Langlois et al., 2000) have both been found to be important indicators of

  4. A User-Oriented Performance Index for Packet Switched Networks.

    DTIC Science & Technology

    1986-03-01

    network type classification is [ Ref . 1] 1. Circuit-switched 2. Message-switched 3. Packet-switched 1. Circuit Switching In circuit switching, a "call" is...tech- niques used for over a century by telegraph and torn paper tape switching systems [ Ref . 2: p. 1307]. These techniques had traditionally used...was an eleven volume analysis written by Paul Baran of the RAND Corporation [ Ref . 3] in 1964. This report showed significant advantages in the use of

  5. Performance Evaluation and Control of Distributed Computer Communication Networks.

    DTIC Science & Technology

    1984-09-01

    in ISDN’s," IEEE Communications Magazine , Feb. 1984. [29] R.A. Pazos-Rangel, "Evaluatidn and Design of Integrated Packet Switch- ing and Circuit... Communications Magazine , February 1984. Abstract The goal of communications network design is to satisfy user requirements with the minimum amount of...investigations have been reported in reference (1) and (2) below. References (1) M. Gerla, R. Pazos-Rangel "Bandwidth Allocation and Routing in ISDN’s," IEEE

  6. Performance of IEEE 1588 in Large-Scale Networks

    DTIC Science & Technology

    2010-11-01

    commercially available Syn1588 network cards from Oregano Systems. They not only feature an IEEE 1588 hardware timestamper, but also a 1 PPS output...of the measurements, which are done per default once a second, can be removed considering the short-term stability of the oscillator. As the Oregano ...PTTI) Meeting 76 ACKNOWLEDGMENTS The authors wish to thank Julien Ridoux, the University of Melbourne, Oregano Systems, and Meinberg for

  7. Traffic Dimensioning and Performance Modeling of 4G LTE Networks

    ERIC Educational Resources Information Center

    Ouyang, Ye

    2011-01-01

    Rapid changes in mobile techniques have always been evolutionary, and the deployment of 4G Long Term Evolution (LTE) networks will be the same. It will be another transition from Third Generation (3G) to Fourth Generation (4G) over a period of several years, as is the case still with the transition from Second Generation (2G) to 3G. As a result,…

  8. High performance interconnection between high data rate networks

    NASA Technical Reports Server (NTRS)

    Foudriat, E. C.; Maly, K.; Overstreet, C. M.; Zhang, L.; Sun, W.

    1992-01-01

    The bridge/gateway system needed to interconnect a wide range of computer networks to support a wide range of user quality-of-service requirements is discussed. The bridge/gateway must handle a wide range of message types including synchronous and asynchronous traffic, large, bursty messages, short, self-contained messages, time critical messages, etc. It is shown that messages can be classified into three basic classes, synchronous and large and small asynchronous messages. The first two require call setup so that packet identification, buffer handling, etc. can be supported in the bridge/gateway. Identification enables resequences in packet size. The third class is for messages which do not require call setup. Resequencing hardware based to handle two types of resequencing problems is presented. The first is for a virtual parallel circuit which can scramble channel bytes. The second system is effective in handling both synchronous and asynchronous traffic between networks with highly differing packet sizes and data rates. The two other major needs for the bridge/gateway are congestion and error control. A dynamic, lossless congestion control scheme which can easily support effective error correction is presented. Results indicate that the congestion control scheme provides close to optimal capacity under congested conditions. Under conditions where error may develop due to intervening networks which are not lossless, intermediate error recovery and correction takes 1/3 less time than equivalent end-to-end error correction under similar conditions.

  9. Functional Connectivity in Multiple Cortical Networks Is Associated with Performance Across Cognitive Domains in Older Adults

    PubMed Central

    Shaw, Emily E.; Schultz, Aaron P.; Sperling, Reisa A.

    2015-01-01

    Abstract Intrinsic functional connectivity MRI has become a widely used tool for measuring integrity in large-scale cortical networks. This study examined multiple cortical networks using Template-Based Rotation (TBR), a method that applies a priori network and nuisance component templates defined from an independent dataset to test datasets of interest. A priori templates were applied to a test dataset of 276 older adults (ages 65–90) from the Harvard Aging Brain Study to examine the relationship between multiple large-scale cortical networks and cognition. Factor scores derived from neuropsychological tests represented processing speed, executive function, and episodic memory. Resting-state BOLD data were acquired in two 6-min acquisitions on a 3-Tesla scanner and processed with TBR to extract individual-level metrics of network connectivity in multiple cortical networks. All results controlled for data quality metrics, including motion. Connectivity in multiple large-scale cortical networks was positively related to all cognitive domains, with a composite measure of general connectivity positively associated with general cognitive performance. Controlling for the correlations between networks, the frontoparietal control network (FPCN) and executive function demonstrated the only significant association, suggesting specificity in this relationship. Further analyses found that the FPCN mediated the relationships of the other networks with cognition, suggesting that this network may play a central role in understanding individual variation in cognition during aging. PMID:25827242

  10. Social Networks and Students' Performance in Secondary Schools: Lessons from an Open Learning Centre, Kenya

    ERIC Educational Resources Information Center

    Muhingi, Wilkins Ndege; Mutavi, Teresia; Kokonya, Donald; Simiyu, Violet Nekesa; Musungu, Ben; Obondo, Anne; Kuria, Mary Wangari

    2015-01-01

    Given the known positive and negative effects of uncontrolled social networking among secondary school students worldwide, it is necessary to establish the relationship between social network sites and academic performances among secondary school students. This study, therefore, aimed at establishing the relationship between secondary school…

  11. Social Networks, Communication Styles, and Learning Performance in a CSCL Community

    ERIC Educational Resources Information Center

    Cho, Hichang; Gay, Geri; Davidson, Barry; Ingraffea, Anthony

    2007-01-01

    The aim of this study is to empirically investigate the relationships between communication styles, social networks, and learning performance in a computer-supported collaborative learning (CSCL) community. Using social network analysis (SNA) and longitudinal survey data, we analyzed how 31 distributed learners developed collaborative learning…

  12. Performance of the Birmingham Solar-Oscillations Network (BiSON)

    NASA Astrophysics Data System (ADS)

    Hale, S. J.; Howe, R.; Chaplin, W. J.; Davies, G. R.; Elsworth, Y. P.

    2016-01-01

    The Birmingham Solar-Oscillations Network (BiSON) has been operating with a full complement of six stations since 1992. Over 20 years later, we look back on the network history. The meta-data from the sites have been analysed to assess performance in terms of site insolation, with a brief look at the challenges that have been encountered over the years. We explain how the international community can gain easy access to the ever-growing dataset produced by the network, and finally look to the future of the network and the potential impact of nearly 25 years of technology miniaturisation.

  13. Enabling Secure High-Performance Wireless Ad Hoc Networking

    DTIC Science & Technology

    2003-05-29

    destinations, consuming energy and available bandwidth. An attacker may similarly create a routing black hole , in which all packets are dropped: by sending...all nodes in an area of the network to point “into” that area when in fact the destination is outside the area. As a special case of a black hole , an...may attempt the following attacks: • Create a gray hole or black hole by removing nodes in a ROUTE REQUEST; however, the per- hop hash mechanism in

  14. Information Fusion and Performance Modeling with Distributed Sensor Networks

    DTIC Science & Technology

    2010-11-01

    Ec,Ed) = X I P(X j I,Ec)P(I j E): (20) SUN & CHANG: MESSAGE PASSING FOR HYBRID BNS: REPRESENTATION, PROPAGATION, AND INTEGRATION 1531 Fig. 6. GHM -2...experiments. One is shown in Fig. 4 as mentioned in Section IIIA called GHM -1. GHM -1 has one loop in each network segment, respectively, (partitioned by...the interface node K). Another experiment model is shown in Fig. 6 called GHM -2. GHM -2 has multiple loops in the continuous segment. For GHM -1, we

  15. Low Temperature Performance of High-Speed Neural Network Circuits

    NASA Technical Reports Server (NTRS)

    Duong, T.; Tran, M.; Daud, T.; Thakoor, A.

    1995-01-01

    Artificial neural networks, derived from their biological counterparts, offer a new and enabling computing paradigm specially suitable for such tasks as image and signal processing with feature classification/object recognition, global optimization, and adaptive control. When implemented in fully parallel electronic hardware, it offers orders of magnitude speed advantage. Basic building blocks of the new architecture are the processing elements called neurons implemented as nonlinear operational amplifiers with sigmoidal transfer function, interconnected through weighted connections called synapses implemented using circuitry for weight storage and multiply functions either in an analog, digital, or hybrid scheme.

  16. Altered small-world brain networks in schizophrenia patients during working memory performance.

    PubMed

    He, Hao; Sui, Jing; Yu, Qingbao; Turner, Jessica A; Ho, Beng-Choon; Sponheim, Scott R; Manoach, Dara S; Clark, Vincent P; Calhoun, Vince D

    2012-01-01

    Impairment of working memory (WM) performance in schizophrenia patients (SZ) is well-established. Compared to healthy controls (HC), SZ patients show aberrant blood oxygen level dependent (BOLD) activations and disrupted functional connectivity during WM performance. In this study, we examined the small-world network metrics computed from functional magnetic resonance imaging (fMRI) data collected as 35 HC and 35 SZ performed a Sternberg Item Recognition Paradigm (SIRP) at three WM load levels. Functional connectivity networks were built by calculating the partial correlation on preprocessed time courses of BOLD signal between task-related brain regions of interest (ROIs) defined by group independent component analysis (ICA). The networks were then thresholded within the small-world regime, resulting in undirected binarized small-world networks at different working memory loads. Our results showed: 1) at the medium WM load level, the networks in SZ showed a lower clustering coefficient and less local efficiency compared with HC; 2) in SZ, most network measures altered significantly as the WM load level increased from low to medium and from medium to high, while the network metrics were relatively stable in HC at different WM loads; and 3) the altered structure at medium WM load in SZ was related to their performance during the task, with longer reaction time related to lower clustering coefficient and lower local efficiency. These findings suggest brain connectivity in patients with SZ was more diffuse and less strongly linked locally in functional network at intermediate level of WM when compared to HC. SZ show distinctly inefficient and variable network structures in response to WM load increase, comparing to stable highly clustered network topologies in HC.

  17. Study on multiple-hops performance of MOOC sequences-based optical labels for OPS networks

    NASA Astrophysics Data System (ADS)

    Zhang, Chongfu; Qiu, Kun; Ma, Chunli

    2009-11-01

    In this paper, we utilize a new study method that is under independent case of multiple optical orthogonal codes to derive the probability function of MOOCS-OPS networks, discuss the performance characteristics for a variety of parameters, and compare some characteristics of the system employed by single optical orthogonal code or multiple optical orthogonal codes sequences-based optical labels. The performance of the system is also calculated, and our results verify that the method is effective. Additionally it is found that performance of MOOCS-OPS networks would, negatively, be worsened, compared with single optical orthogonal code-based optical label for optical packet switching (SOOC-OPS); however, MOOCS-OPS networks can greatly enlarge the scalability of optical packet switching networks.

  18. Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

    NASA Technical Reports Server (NTRS)

    Kobayashi, Takahisa; Simon, Donald L.

    2002-01-01

    As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

  19. A Method for Integrating Thrust-Vectoring and Actuated Forebody Strakes with Conventional Aerodynamic Controls on a High-Performance Fighter Airplane

    NASA Technical Reports Server (NTRS)

    Lallman, Frederick J.; Davidson, John B.; Murphy, Patrick C.

    1998-01-01

    A method, called pseudo controls, of integrating several airplane controls to achieve cooperative operation is presented. The method eliminates conflicting control motions, minimizes the number of feedback control gains, and reduces the complication of feedback gain schedules. The method is applied to the lateral/directional controls of a modified high-performance airplane. The airplane has a conventional set of aerodynamic controls, an experimental set of thrust-vectoring controls, and an experimental set of actuated forebody strakes. The experimental controls give the airplane additional control power for enhanced stability and maneuvering capabilities while flying over an expanded envelope, especially at high angles of attack. The flight controls are scheduled to generate independent body-axis control moments. These control moments are coordinated to produce stability-axis angular accelerations. Inertial coupling moments are compensated. Thrust-vectoring controls are engaged according to their effectiveness relative to that of the aerodynamic controls. Vane-relief logic removes steady and slowly varying commands from the thrust-vectoring controls to alleviate heating of the thrust turning devices. The actuated forebody strakes are engaged at high angles of attack. This report presents the forward-loop elements of a flight control system that positions the flight controls according to the desired stability-axis accelerations. This report does not include the generation of the required angular acceleration commands by means of pilot controls or the feedback of sensed airplane motions.

  20. Mapping the social network: tracking lice in a wild primate (Microcebus rufus) population to infer social contacts and vector potential

    PubMed Central

    2012-01-01

    previously unseen parasite movement between lemurs, but also allowed us to infer social interactions between them. As lice are known pathogen vectors, our method also allowed us to identify the lemurs most likely to facilitate louse-mediated epidemics. Our approach demonstrates the potential to uncover otherwise inaccessible parasite-host, and host social interaction data in any trappable species parasitized by sucking lice. PMID:22449178

  1. Analysis of latency performance of bluetooth low energy (BLE) networks.

    PubMed

    Cho, Keuchul; Park, Woojin; Hong, Moonki; Park, Gisu; Cho, Wooseong; Seo, Jihoon; Han, Kijun

    2014-12-23

    Bluetooth Low Energy (BLE) is a short-range wireless communication technology aiming at low-cost and low-power communication. The performance evaluation of classical Bluetooth device discovery have been intensively studied using analytical modeling and simulative methods, but these techniques are not applicable to BLE, since BLE has a fundamental change in the design of the discovery mechanism, including the usage of three advertising channels. Recently, there several works have analyzed the topic of BLE device discovery, but these studies are still far from thorough. It is thus necessary to develop a new, accurate model for the BLE discovery process. In particular, the wide range settings of the parameters introduce lots of potential for BLE devices to customize their discovery performance. This motivates our study of modeling the BLE discovery process and performing intensive simulation. This paper is focused on building an analytical model to investigate the discovery probability, as well as the expected discovery latency, which are then validated via extensive experiments. Our analysis considers both continuous and discontinuous scanning modes. We analyze the sensitivity of these performance metrics to parameter settings to quantitatively examine to what extent parameters influence the performance metric of the discovery processes.

  2. Cloning vector

    DOEpatents

    Guilfoyle, R.A.; Smith, L.M.

    1994-12-27

    A vector comprising a filamentous phage sequence containing a first copy of filamentous phage gene X and other sequences necessary for the phage to propagate is disclosed. The vector also contains a second copy of filamentous phage gene X downstream from a promoter capable of promoting transcription in a bacterial host. In a preferred form of the present invention, the filamentous phage is M13 and the vector additionally includes a restriction endonuclease site located in such a manner as to substantially inactivate the second gene X when a DNA sequence is inserted into the restriction site. 2 figures.

  3. Cloning vector

    DOEpatents

    Guilfoyle, Richard A.; Smith, Lloyd M.

    1994-01-01

    A vector comprising a filamentous phage sequence containing a first copy of filamentous phage gene X and other sequences necessary for the phage to propagate is disclosed. The vector also contains a second copy of filamentous phage gene X downstream from a promoter capable of promoting transcription in a bacterial host. In a preferred form of the present invention, the filamentous phage is M13 and the vector additionally includes a restriction endonuclease site located in such a manner as to substantially inactivate the second gene X when a DNA sequence is inserted into the restriction site.

  4. Social learning strategies modify the effect of network structure on group performance

    PubMed Central

    Barkoczi, Daniel; Galesic, Mirta

    2016-01-01

    The structure of communication networks is an important determinant of the capacity of teams, organizations and societies to solve policy, business and science problems. Yet, previous studies reached contradictory results about the relationship between network structure and performance, finding support for the superiority of both well-connected efficient and poorly connected inefficient network structures. Here we argue that understanding how communication networks affect group performance requires taking into consideration the social learning strategies of individual team members. We show that efficient networks outperform inefficient networks when individuals rely on conformity by copying the most frequent solution among their contacts. However, inefficient networks are superior when individuals follow the best member by copying the group member with the highest payoff. In addition, groups relying on conformity based on a small sample of others excel at complex tasks, while groups following the best member achieve greatest performance for simple tasks. Our findings reconcile contradictory results in the literature and have broad implications for the study of social learning across disciplines. PMID:27713417

  5. Social learning strategies modify the effect of network structure on group performance.

    PubMed

    Barkoczi, Daniel; Galesic, Mirta

    2016-10-07

    The structure of communication networks is an important determinant of the capacity of teams, organizations and societies to solve policy, business and science problems. Yet, previous studies reached contradictory results about the relationship between network structure and performance, finding support for the superiority of both well-connected efficient and poorly connected inefficient network structures. Here we argue that understanding how communication networks affect group performance requires taking into consideration the social learning strategies of individual team members. We show that efficient networks outperform inefficient networks when individuals rely on conformity by copying the most frequent solution among their contacts. However, inefficient networks are superior when individuals follow the best member by copying the group member with the highest payoff. In addition, groups relying on conformity based on a small sample of others excel at complex tasks, while groups following the best member achieve greatest performance for simple tasks. Our findings reconcile contradictory results in the literature and have broad implications for the study of social learning across disciplines.

  6. Social learning strategies modify the effect of network structure on group performance

    NASA Astrophysics Data System (ADS)

    Barkoczi, Daniel; Galesic, Mirta

    2016-10-01

    The structure of communication networks is an important determinant of the capacity of teams, organizations and societies to solve policy, business and science problems. Yet, previous studies reached contradictory results about the relationship between network structure and performance, finding support for the superiority of both well-connected efficient and poorly connected inefficient network structures. Here we argue that understanding how communication networks affect group performance requires taking into consideration the social learning strategies of individual team members. We show that efficient networks outperform inefficient networks when individuals rely on conformity by copying the most frequent solution among their contacts. However, inefficient networks are superior when individuals follow the best member by copying the group member with the highest payoff. In addition, groups relying on conformity based on a small sample of others excel at complex tasks, while groups following the best member achieve greatest performance for simple tasks. Our findings reconcile contradictory results in the literature and have broad implications for the study of social learning across disciplines.

  7. A comparison of back propagation and Generalized Regression Neural Networks performance in neutron spectrometry.

    PubMed

    Martínez-Blanco, Ma Del Rosario; Ornelas-Vargas, Gerardo; Solís-Sánchez, Luis Octavio; Castañeda-Miranada, Rodrigo; Vega-Carrillo, Héctor René; Celaya-Padilla, José M; Garza-Veloz, Idalia; Martínez-Fierro, Margarita; Ortiz-Rodríguez, José Manuel

    2016-11-01

    The process of unfolding the neutron energy spectrum has been subject of research for many years. Monte Carlo, iterative methods, the bayesian theory, the principle of maximum entropy are some of the methods used. The drawbacks associated with traditional unfolding procedures have motivated the research of complementary approaches. Back Propagation Neural Networks (BPNN), have been applied with success in neutron spectrometry and dosimetry domains, however, the structure and learning parameters are factors that highly impact in the networks performance. In ANN domain, Generalized Regression Neural Network (GRNN) is one of the simplest neural networks in term of network architecture and learning algorithm. The learning is instantaneous, requiring no time for training. Opposite to BPNN, a GRNN would be formed instantly with just a 1-pass training on the development data. In the network development phase, the only hurdle is to optimize the hyper-parameter, which is known as sigma, governing the smoothness of the network. The aim of this work was to compare the performance of BPNN and GRNN in the solution of the neutron spectrometry problem. From results obtained it can be observed that despite the very similar results, GRNN performs better than BPNN.

  8. High-performance parallel interface to synchronous optical network gateway

    DOEpatents

    St. John, Wallace B.; DuBois, David H.

    1996-01-01

    A system of sending and receiving gateways interconnects high speed data interfaces, e.g., HIPPI interfaces, through fiber optic links, e.g., a SONET network. An electronic stripe distributor distributes bytes of data from a first interface at the sending gateway onto parallel fiber optics of the fiber optic link to form transmitted data. An electronic stripe collector receives the transmitted data on the parallel fiber optics and reforms the data into a format effective for input to a second interface at the receiving gateway. Preferably, an error correcting syndrome is constructed at the sending gateway and sent with a data frame so that transmission errors can be detected and corrected in a real-time basis. Since the high speed data interface operates faster than any of the fiber optic links the transmission rate must be adapted to match the available number of fiber optic links so the sending and receiving gateways monitor the availability of fiber links and adjust the data throughput accordingly. In another aspect, the receiving gateway must have sufficient available buffer capacity to accept an incoming data frame. A credit-based flow control system provides for continuously updating the sending gateway on the available buffer capacity at the receiving gateway.

  9. High-performance parallel interface to synchronous optical network gateway

    DOEpatents

    St. John, W.B.; DuBois, D.H.

    1996-12-03

    Disclosed is a system of sending and receiving gateways interconnects high speed data interfaces, e.g., HIPPI interfaces, through fiber optic links, e.g., a SONET network. An electronic stripe distributor distributes bytes of data from a first interface at the sending gateway onto parallel fiber optics of the fiber optic link to form transmitted data. An electronic stripe collector receives the transmitted data on the parallel fiber optics and reforms the data into a format effective for input to a second interface at the receiving gateway. Preferably, an error correcting syndrome is constructed at the sending gateway and sent with a data frame so that transmission errors can be detected and corrected in a real-time basis. Since the high speed data interface operates faster than any of the fiber optic links the transmission rate must be adapted to match the available number of fiber optic links so the sending and receiving gateways monitor the availability of fiber links and adjust the data throughput accordingly. In another aspect, the receiving gateway must have sufficient available buffer capacity to accept an incoming data frame. A credit-based flow control system provides for continuously updating the sending gateway on the available buffer capacity at the receiving gateway. 7 figs.

  10. 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…

  11. Dynamic Social Networks in High Performance Football Coaching

    ERIC Educational Resources Information Center

    Occhino, Joseph; Mallett, Cliff; Rynne, Steven

    2013-01-01

    Background: Sports coaching is largely a social activity where engagement with athletes and support staff can enhance the experiences for all involved. This paper examines how high performance football coaches develop knowledge through their interactions with others within a social learning theory framework. Purpose: The key purpose of this study…

  12. System for Automated Calibration of Vector Modulators

    NASA Technical Reports Server (NTRS)

    Lux, James; Boas, Amy; Li, Samuel

    2009-01-01

    Vector modulators are used to impose baseband modulation on RF signals, but non-ideal behavior limits the overall performance. The non-ideal behavior of the vector modulator is compensated using data collected with the use of an automated test system driven by a LabVIEW program that systematically applies thousands of control-signal values to the device under test and collects RF measurement data. The technology innovation automates several steps in the process. First, an automated test system, using computer controlled digital-to-analog converters (DACs) and a computer-controlled vector network analyzer (VNA) systematically can apply different I and Q signals (which represent the complex number by which the RF signal is multiplied) to the vector modulator under test (VMUT), while measuring the RF performance specifically, gain and phase. The automated test system uses the LabVIEW software to control the test equipment, collect the data, and write it to a file. The input to the Lab - VIEW program is either user-input for systematic variation, or is provided in a file containing specific test values that should be fed to the VMUT. The output file contains both the control signals and the measured data. The second step is to post-process the file to determine the correction functions as needed. The result of the entire process is a tabular representation, which allows translation of a desired I/Q value to the required analog control signals to produce a particular RF behavior. In some applications, corrected performance is needed only for a limited range. If the vector modulator is being used as a phase shifter, there is only a need to correct I and Q values that represent points on a circle, not the entire plane. This innovation has been used to calibrate 2-GHz MMIC (monolithic microwave integrated circuit) vector modulators in the High EIRP Cluster Array project (EIRP is high effective isotropic radiated power). These calibrations were then used to create

  13. 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.

  14. Performance Analysis of MIMO Relay Network via Propagation Measurement in L-Shaped Corridor Environment

    NASA Astrophysics Data System (ADS)

    Lertwiram, Namzilp; Tran, Gia Khanh; Mizutani, Keiichi; Sakaguchi, Kei; Araki, Kiyomichi

    Setting relays can address the shadowing problem between a transmitter (Tx) and a receiver (Rx). Moreover, the Multiple-Input Multiple-Output (MIMO) technique has been introduced to improve wireless link capacity. The MIMO technique can be applied in relay network to enhance system performance. However, the efficiency of relaying schemes and relay placement have not been well investigated with experiment-based study. This paper provides a propagation measurement campaign of a MIMO two-hop relay network in 5GHz band in an L-shaped corridor environment with various relay locations. Furthermore, this paper proposes a Relay Placement Estimation (RPE) scheme to identify the optimum relay location, i.e. the point at which the network performance is highest. Analysis results of channel capacity show that relaying technique is beneficial over direct transmission in strong shadowing environment while it is ineffective in non-shadowing environment. In addition, the optimum relay location estimated with the RPE scheme also agrees with the location where the network achieves the highest performance as identified by network capacity. Finally, the capacity analysis shows that two-way MIMO relay employing network coding has the best performance while cooperative relaying scheme is not effective due to shadowing effect weakening the signal strength of the direct link.

  15. INCITE: Edge-based Traffic Processing and Inference for High-Performance Networks

    SciTech Connect

    Baraniuk, Richard G.; Feng, Wu-chun; Cottrell, Les; Knightly, Edward; Nowak, Robert; Riedi, Rolf

    2005-06-20

    The INCITE (InterNet Control and Inference Tools at the Edge) Project developed on-line tools to characterize and map host and network performance as a function of space, time, application, protocol, and service. In addition to their utility for trouble-shooting problems, these tools will enable a new breed of applications and operating systems that are network aware and resource aware. Launching from the foundation provided our recent leading-edge research on network measurement, multifractal signal analysis, multiscale random fields, and quality of service, our effort consisted of three closely integrated research thrusts that directly attack several key networking challenges of DOE's SciDAC program. These are: Thrust 1, Multiscale traffic analysis and modeling techniques; Thrust 2, Inference and control algorithms for network paths, links, and routers, and Thrust 3, Data collection tools.

  16. Neural Network for Visual Search Classification

    DTIC Science & Technology

    2007-11-02

    neural network used to perform visual search classification. The neural network consists of a Learning vector quantization network (LVQ) and a single layer perceptron. The objective of this neural network is to classify the various human visual search patterns into predetermined classes. The classes signify the different search strategies used by individuals to scan the same target pattern. The input search patterns are quantified with respect to an ideal search pattern, determined by the user. A supervised learning rule,

  17. Performance Analysis of Network Model to Identify Healthy and Cancerous Colon Genes.

    PubMed

    Roy, Tanusree; Barman, Soma

    2016-03-01

    Modeling of cancerous and healthy Homo Sapiens colon gene using electrical network is proposed to study their behavior. In this paper, the individual amino acid models are designed using hydropathy index of amino acid side chain. The phase and magnitude responses of genes are examined to screen out cancer from healthy genes. The performance of proposed modeling technique is judged using various performance measurement metrics such as accuracy, sensitivity, specificity, etc. The network model performance is increased with frequency, which is analyzed using the receiver operating characteristic curve. The accuracy of the model is tested on colon genes and achieved maximum 97% at 10-MHz frequency.

  18. The Impact of Network Performance on Warfighter Effectiveness

    DTIC Science & Technology

    2006-01-01

    necessarily make greater gains in an operation than the choice of appropriate tactics.” Organization of This Report The remainder of this report is...Likelihood Blue Objective Achieved 34% 39% 38% A new model was fit using the logit function (see Appendix A) so that the perform- ance metric is now the...again reached conclusions that were similar to what this re- port also found, i.e., “the force with improved situational awareness can only take

  19. Performance analysis and comparison of PTOP and LANE for IP transmission over ATM networks

    NASA Astrophysics Data System (ADS)

    Zubairi, Junaid A.; Al-Irhayim, Sufyan; Al-Khateeb, Wajdi; Wajdi, Yahya

    1998-12-01

    Due to its traffic control and performance assurance characteristics, ATM is being employed as the core network in most campuses. However, bulk of the workstations remain on Ethernet, generating IP traffic that passes through ATM using special schemes such as PTOP or LANE. In such a network, the performance is affected due to the extra overheads in multiple conversions between cells and packets and managing virtual circuits. The aim of this paper is to compare the performance of PTOP and LANE in passing the IP traffic under various conditions. This study helps in understanding the various performance issues in these environments in order to define the end-to-end quality of service for Ethernet-ATM networks.

  20. Research Activity in Computational Physics utilizing High Performance Computing: Co-authorship Network Analysis

    NASA Astrophysics Data System (ADS)

    Ahn, Sul-Ah; Jung, Youngim

    2016-10-01

    The research activities of the computational physicists utilizing high performance computing are analyzed by bibliometirc approaches. This study aims at providing the computational physicists utilizing high-performance computing and policy planners with useful bibliometric results for an assessment of research activities. In order to achieve this purpose, we carried out a co-authorship network analysis of journal articles to assess the research activities of researchers for high-performance computational physics as a case study. For this study, we used journal articles of the Scopus database from Elsevier covering the time period of 2004-2013. We extracted the author rank in the physics field utilizing high-performance computing by the number of papers published during ten years from 2004. Finally, we drew the co-authorship network for 45 top-authors and their coauthors, and described some features of the co-authorship network in relation to the author rank. Suggestions for further studies are discussed.

  1. Support vector regression and artificial neural network models for stability indicating analysis of mebeverine hydrochloride and sulpiride mixtures in pharmaceutical preparation: A comparative study

    NASA Astrophysics Data System (ADS)

    Naguib, Ibrahim A.; Darwish, Hany W.

    2012-02-01

    A comparison between support vector regression (SVR) and Artificial Neural Networks (ANNs) multivariate regression methods is established showing the underlying algorithm for each and making a comparison between them to indicate the inherent advantages and limitations. In this paper we compare SVR to ANN with and without variable selection procedure (genetic algorithm (GA)). To project the comparison in a sensible way, the methods are used for the stability indicating quantitative analysis of mixtures of mebeverine hydrochloride and sulpiride in binary mixtures as a case study in presence of their reported impurities and degradation products (summing up to 6 components) in raw materials and pharmaceutical dosage form via handling the UV spectral data. For proper analysis, a 6 factor 5 level experimental design was established resulting in a training set of 25 mixtures containing different ratios of the interfering species. An independent test set consisting of 5 mixtures was used to validate the prediction ability of the suggested models. The proposed methods (linear SVR (without GA) and linear GA-ANN) were successfully applied to the analysis of pharmaceutical tablets containing mebeverine hydrochloride and sulpiride mixtures. The results manifest the problem of nonlinearity and how models like the SVR and ANN can handle it. The methods indicate the ability of the mentioned multivariate calibration models to deconvolute the highly overlapped UV spectra of the 6 components' mixtures, yet using cheap and easy to handle instruments like the UV spectrophotometer.

  2. Analogies between the measurement of acoustic impedance via the reaction on the source method and the automatic microwave vector network analyzer technique

    NASA Astrophysics Data System (ADS)

    McLean, James; Sutton, Robert; Post, John

    2003-10-01

    One useful method of acoustic impedance measurement involves the measurement of the electrical impedance ``looking into'' the electrical port of a reciprocal electroacoustic transducer. This reaction on the source method greatly facilitates the measurement of acoustic impedance by borrowing highly refined techniques to measure electrical impedance. It is also well suited for in situ acoustic impedance measurements. In order to accurately determine acoustic impedance from the measured electrical impedance, the characteristics of the transducer must be accurately known, i.e., the characteristics of the transducer must be ``removed'' completely from the data. The measurement of acoustic impedance via the measurement of the reaction on the source is analogous to modern microwave measurements made with an automatic vector network analyzer. The action of the analyzer is described as de-embedding the desired data (such as acoustic impedance) from the raw data. Such measurements are fundamentally substitution measurements in that the transducer's characteristics are determined by measuring a set of reference standards. The reaction on the source method is extended to take advantage of improvements in microwave measurement techniques which allow calibration via imperfect standard loads. This removes one of the principal weaknesses of the method in that the requirement of high-quality reference standards is relaxed.

  3. A performance analysis of DS-CDMA and SCPC VSAT networks

    NASA Technical Reports Server (NTRS)

    Hayes, David P.; Ha, Tri T.

    1990-01-01

    Spread-spectrum and single-channel-per-carrier (SCPC) transmission techniques work well in very small aperture terminal (VSAT) networks for multiple-access purposes while allowing the earth station antennas to remain small. Direct-sequence code-division multiple-access (DS-CDMA) is the simplest spread-spectrum technique to use in a VSAT network since a frequency synthesizer is not required for each terminal. An examination is made of the DS-CDMA and SCPC Ku-band VSAT satellite systems for low-density (64-kb/s or less) communications. A method for improving the standardf link analysis of DS-CDMA satellite-switched networks by including certain losses is developed. The performance of 50-channel full mesh and star network architectures is analyzed. The selection of operating conditions producing optimum performance is demonstrated.

  4. Prediction of Biological Motion Perception Performance from Intrinsic Brain Network Regional Efficiency

    PubMed Central

    Wang, Zengjian; Zhang, Delong; Liang, Bishan; Chang, Song; Pan, Jinghua; Huang, Ruiwang; Liu, Ming

    2016-01-01

    Biological motion perception (BMP) refers to the ability to perceive the moving form of a human figure from a limited amount of stimuli, such as from a few point lights located on the joints of a moving body. BMP is commonplace and important, but there is great inter-individual variability in this ability. This study used multiple regression model analysis to explore the association between BMP performance and intrinsic brain activity, in order to investigate the neural substrates underlying inter-individual variability of BMP performance. The resting-state functional magnetic resonance imaging (rs-fMRI) and BMP performance data were collected from 24 healthy participants, for whom intrinsic brain networks were constructed, and a graph-based network efficiency metric was measured. Then, a multiple linear regression model was used to explore the association between network regional efficiency and BMP performance. We found that the local and global network efficiency of many regions was significantly correlated with BMP performance. Further analysis showed that the local efficiency rather than global efficiency could be used to explain most of the BMP inter-individual variability, and the regions involved were predominately located in the Default Mode Network (DMN). Additionally, discrimination analysis showed that the local efficiency of certain regions such as the thalamus could be used to classify BMP performance across participants. Notably, the association pattern between network nodal efficiency and BMP was different from the association pattern of static directional/gender information perception. Overall, these findings show that intrinsic brain network efficiency may be considered a neural factor that explains BMP inter-individual variability. PMID:27853427

  5. HPNAIDM: The High-Performance Network Anomaly/Intrusion Detection and Mitigation System

    SciTech Connect

    Chen, Yan

    2013-12-05

    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 is 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.

  6. Crystal networks in silk fibrous materials: from hierarchical structure to ultra performance.

    PubMed

    Nguyen, Anh Tuan; Huang, Qiao-Ling; Yang, Zhen; Lin, Naibo; Xu, Gangqin; Liu, Xiang Yang

    2015-03-01

    This review provides a comprehensive survey of the structural characteristics of crystal networks of silk soft fibrous materials in correlation with the macroscopic properties/performance and the network formation mechanisms. The correlation between the hierarchical mesoscopic structures and the mechanical properties of silk soft fibrous materials including silk fibroin hydrogels and naturally spun silk fibers are addressed based on the hierarchical crystal network models. Namely, two types of hierarchical networks are identified: the weak nanofibril-nanofibril interaction case (i.e., silk fibroin hydrogels), and the strong nanofibril-nanofibril interaction case (i.e., silk fibers). The macroscopic properties, i.e., the rheological/mechanical properties, can be controlled in terms of tuning different levels of hierarchical network structures by ultrasonication-induced gelation, introducing the initial nucleation centers, etc. Such controls take effect by different mesoscale assembly pathways, which are found to occur via different routes of the nucleation and growth processes. Furthermore, the hierarchical network model of soft fibrous materials can be applied to explain the superior mechanical properties and the unique strain-hardening behaviors of spider silk fibers within the framework of hierarchical breaking mechanism. Obviously, a knowledge of crystal networks will allow the prediction of the performance and engineering strategy of silk fibrous materials in generals.

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

    SciTech Connect

    Potok, Thomas E; Schuman, Catherine D; Young, Steven R; Patton, Robert M; Spedalieri, Federico; Liu, Jeremy; Yao, Ke-Thia; Rose, Garrett; Chakma, Gangotree

    2016-01-01

    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 determine 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.

  8. Molten carbonate fuel cell networks: Principles, analysis, and performance. Technical note

    SciTech Connect

    Wimer, J.G.; Williams, M.C.

    1993-01-01

    The chemical reactions in an internally reforming molten carbonate fuel cell (IRMCFC) are described and combined into the overall IRMCFC reaction. Thermodynamic and electrochemical principles are discussed, and structure and operation of fuel cell stacks are explained. In networking, multiple fuel cell stacks are arranged so that reactant streams are fed and recycled through stacks in series, for higher reactant utilization and increased system efficiency. Advantages and performance of networked and conventional systems are compared, using ASPEN simulations. The concept of networking can be applied to any electrochemical membrane, such as that developed for hot gas cleanup in future power plants. 2 tabs, 16 figs, 9 refs.

  9. Laser ranging network performance and routine orbit determination at D-PAF

    NASA Technical Reports Server (NTRS)

    Massmann, Franz-Heinrich; Reigber, C.; Li, H.; Koenig, Rolf; Raimondo, J. C.; Rajasenan, C.; Vei, M.

    1993-01-01

    ERS-1 is now about 8 months in orbit and has been tracked by the global laser network from the very beginning of the mission. The German processing and archiving facility for ERS-1 (D-PAF) is coordinating and supporting the network and performing the different routine orbit determination tasks. This paper presents details about the global network status, the communication to D-PAF and the tracking data and orbit processing system at D-PAF. The quality of the preliminary and precise orbits are shown and some problem areas are identified.

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

    DTIC Science & Technology

    2011-08-01

    Modeling and Performance Evaluation of Backoff Misbehaving Nodes in CSMA/CA Networks Zhuo Lu, Student Member, IEEE, Wenye Wang, Senior Member, IEEE...and Cliff Wang Abstract—Backoff misbehavior , in which a wireless node deliberately manipulates its backoff time, can induce significant network... misbehavior , little attention has been focused on quantifying the gain of backoff misbehaviors . In this paper, to assess the gain that misbehaving nodes can

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

    PubMed Central

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

    2017-01-01

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

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

    PubMed

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

    2017-02-01

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

  13. 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.

  14. 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

  15. Performance of wavelet analysis and neural networks for pathological voices identification

    NASA Astrophysics Data System (ADS)

    Salhi, Lotfi; Talbi, Mourad; Abid, Sabeur; Cherif, Adnane

    2011-09-01

    Within the medical environment, diverse techniques exist to assess the state of the voice of the patient. The inspection technique is inconvenient for a number of reasons, such as its high cost, the duration of the inspection, and above all, the fact that it is an invasive technique. This study focuses on a robust, rapid and accurate system for automatic identification of pathological voices. This system employs non-invasive, non-expensive and fully automated method based on hybrid approach: wavelet transform analysis and neural network classifier. First, we present the results obtained in our previous study while using classic feature parameters. These results allow visual identification of pathological voices. Second, quantified parameters drifting from the wavelet analysis are proposed to characterise the speech sample. On the other hand, a system of multilayer neural networks (MNNs) has been developed which carries out the automatic detection of pathological voices. The developed method was evaluated using voice database composed of recorded voice samples (continuous speech) from normophonic or dysphonic speakers. The dysphonic speakers were patients of a National Hospital 'RABTA' of Tunis Tunisia and a University Hospital in Brussels, Belgium. Experimental results indicate a success rate ranging between 75% and 98.61% for discrimination of normal and pathological voices using the proposed parameters and neural network classifier. We also compared the average classification rate based on the MNN, Gaussian mixture model and support vector machines.

  16. [Research on QSPR for n-octanol-water partition coefficients of organic compounds based on genetic algorithms-support vector machine and genetic algorithms-radial basis function neural networks].

    PubMed

    Qi, Jun; Niu, Jun-Feng; Wang, Li-Li

    2008-01-01

    A modified method to develop quantitative structure-property relationship (QSPR) models of organic compounds was proposed based on genetic algorithm (GA) and support vector machine (SVM) (GA-SVM). GA was used to perform the variable selection, and SVM was used to construct QSPR models. GA-SVM was applied to develop the QSPR models for n-octanol-water partition coefficients ( Kow) of 38 typical organic compounds in food industry. 5 descriptors (molecular weights, Hansen polarity, boiling point, percent oxygen and percent hydrogen) were selected in the QSPR model. The coefficient of multiple determination (R2), the sum of squares due to error (SSE) and the root mean squared error (RMSE) values between the measured values and predicted values of the model developed by GA-SVM are 0.999, 0.048 and 0.036, respectively, indicating good predictive capability for lgKow values of these organic compounds. Based on leave-one-out cross validation, the QSPR model constructed by GA-SVM showed good robustness (SSE = 0.295, RMSE = 0.089, R2 = 0.995). Moreover, the models developed by GA-SVM were compared with the models constructed by genetic algorithm-radial basis function neural network (GA-RBFNN) and linear method. The models constructed by GA-SVM show the optimal predictive capability and robustness in the comparison, which illustrates GA-SVM is the optimal method for developing QSPR models for lgKow values of these organic compounds.

  17. Design and analysis of a novel chaotic diagonal recurrent neural network

    NASA Astrophysics Data System (ADS)

    Wang, Libiao; Meng, Zhuo; Sun, Yize; Guo, Lei; Zhou, Mingxing

    2015-09-01

    A chaotic neural network model with logistic mapping is proposed to improve the performance of the conventional diagonal recurrent neural network. The network shows rich dynamic behaviors that contribute to escaping from a local minimum to reach the global minimum easily. Then, a simple parameter modulated chaos controller is adopted to enhance convergence speed of the network. Furthermore, an adaptive learning algorithm with the robust adaptive dead zone vector is designed to improve the generalization performance of the network, and weights convergence for the network with the adaptive dead zone vectors is proved in the sense of Lyapunov functions. Finally, the numerical simulation is carried out to demonstrate the correctness of the theory.

  18. Classification of fault location and the degree of performance degradation of a rolling bearing based on an improved hyper-sphere-structured multi-class support vector machine

    NASA Astrophysics Data System (ADS)

    Wang, Yujing; Kang, Shouqiang; Jiang, Yicheng; Yang, Guangxue; Song, Lixin; Mikulovich, V. I.

    2012-05-01

    Effective classification of a rolling bearing fault location and especially its degree of performance degradation provides an important basis for appropriate fault judgment and processing. Two methods are introduced to extract features of the rolling bearing vibration signal—one combining empirical mode decomposition (EMD) with the autoregressive model, whose model parameters and variances of the remnant can be obtained using the Yule-Walker or Ulrych-Clayton method, and the other combining EMD with singular value decomposition. Feature vector matrices obtained are then regarded as the input of the improved hyper-sphere-structured multi-class support vector machine (HSSMC-SVM) for classification. Thereby, multi-status intelligent diagnosis of normal rolling bearings and faulty rolling bearings at different locations and the degrees of performance degradation of the faulty rolling bearings can be achieved simultaneously. Experimental results show that EMD combined with singular value decomposition and the improved HSSMC-SVM intelligent method requires less time and has a higher recognition rate.

  19. On the Improvement of Convergence Performance for Integrated Design of Wind Turbine Blade Using a Vector Dominating Multi-objective Evolution Algorithm

    NASA Astrophysics Data System (ADS)

    Wang, L.; Wang, T. G.; Wu, J. H.; Cheng, G. P.

    2016-09-01

    A novel multi-objective optimization algorithm incorporating evolution strategies and vector mechanisms, referred as VD-MOEA, is proposed and applied in aerodynamic- structural integrated design of wind turbine blade. In the algorithm, a set of uniformly distributed vectors is constructed to guide population in moving forward to the Pareto front rapidly and maintain population diversity with high efficiency. For example, two- and three- objective designs of 1.5MW wind turbine blade are subsequently carried out for the optimization objectives of maximum annual energy production, minimum blade mass, and minimum extreme root thrust. The results show that the Pareto optimal solutions can be obtained in one single simulation run and uniformly distributed in the objective space, maximally maintaining the population diversity. In comparison to conventional evolution algorithms, VD-MOEA displays dramatic improvement of algorithm performance in both convergence and diversity preservation for handling complex problems of multi-variables, multi-objectives and multi-constraints. This provides a reliable high-performance optimization approach for the aerodynamic-structural integrated design of wind turbine blade.

  20. Copper nanofiber-networked cobalt oxide composites for high performance Li-ion batteries

    PubMed Central

    2011-01-01

    We prepared a composite electrode structure consisting of copper nanofiber-networked cobalt oxide (CuNFs@CoOx). The copper nanofibers (CuNFs) were fabricated on a substrate with formation of a network structure, which may have potential for improving electron percolation and retarding film deformation during the discharging/charging process over the electroactive cobalt oxide. Compared to bare CoOxthin-film (CoOxTF) electrodes, the CuNFs@CoOxelectrodes exhibited a significant enhancement of rate performance by at least six-fold at an input current density of 3C-rate. Such enhanced Li-ion storage performance may be associated with modified electrode structure at the nanoscale, improved charge transfer, and facile stress relaxation from the embedded CuNF network. Consequently, the CuNFs@CoOxcomposite structure demonstrated here can be used as a promising high-performance electrode for Li-ion batteries. PMID:21711839

  1. Performance Analysis of TCP Enhancements in Satellite Data Networks

    NASA Technical Reports Server (NTRS)

    Broyles, Ren H.

    1999-01-01

    This research examines two proposed enhancements to the well-known Transport Control Protocol (TCP) in the presence of noisy communication links. The Multiple Pipes protocol is an application-level adaptation of the standard TCP protocol, where several TCP links cooperate to transfer data. The Space Communication Protocol Standard - Transport Protocol (SCPS-TP) modifies TCP to optimize performance in a satellite environment. While SCPS-TP has inherent advantages that allow it to deliver data more rapidly than Multiple Pipes, the protocol, when optimized for operation in a high-error environment, is not compatible with legacy TCP systems, and requires changes to the TCP specification. This investigation determines the level of improvement offered by SCPS-TP's Corruption Mode, which will help determine if migration to the protocol is appropriate in different environments. As the percentage of corrupted packets approaches 5 %, Multiple Pipes can take over five times longer than SCPS-TP to deliver data. At high error rates, SCPS-TP's advantage is primarily caused by Multiple Pipes' use of congestion control algorithms. The lack of congestion control, however, limits the systems in which SCPS-TP can be effectively used.

  2. Phylogeographic analysis reveals association of tick-borne pathogen, Anaplasma marginale, MSP1a sequences with ecological traits affecting tick vector performance

    PubMed Central

    Estrada-Peña, Agustín; Naranjo, Victoria; Acevedo-Whitehouse, Karina; Mangold, Atilio J; Kocan, Katherine M; de la Fuente, José

    2009-01-01

    Background The tick-borne pathogen Anaplasma marginale, which is endemic worldwide, is the type species of the genus Anaplasma (Rickettsiales: Anaplasmataceae). Rhipicephalus (Boophilus) microplus is the most important tick vector of A. marginale in tropical and subtropical regions of the world. Despite extensive characterization of the genetic diversity in A. marginale geographic strains using major surface protein sequences, little is known about the biogeography and evolution of A. marginale and other Anaplasma species. For A. marginale, MSP1a was shown to be involved in vector-pathogen and host-pathogen interactions and to have evolved under positive selection pressure. The MSP1a of A. marginale strains differs in molecular weight because of a variable number of tandem 23-31 amino acid repeats and has proven to be a stable marker of strain identity. While phylogenetic studies of MSP1a repeat sequences have shown evidence of A. marginale-tick co-evolution, these studies have not provided phylogeographic information on a global scale because of the high level of MSP1a genetic diversity among geographic strains. Results In this study we showed that the phylogeography of A. marginale MSP1a sequences is associated with world ecological regions (ecoregions) resulting in different evolutionary pressures and thence MSP1a sequences. The results demonstrated that the MSP1a first (R1) and last (RL) repeats and microsatellite sequences were associated with world ecoregion clusters with specific and different environmental envelopes. The evolution of R1 repeat sequences was found to be under positive selection. It is hypothesized that the driving environmental factors regulating tick populations could act on the selection of different A. marginale MSP1a sequence lineages, associated to each ecoregion. Conclusion The results reported herein provided the first evidence that the evolution of A. marginale was linked to ecological traits affecting tick vector performance. These

  3. Centrality and charisma: comparing how leader networks and attributions affect team performance.

    PubMed

    Balkundi, Prasad; Kilduff, Martin; Harrison, David A

    2011-11-01

    When leaders interact in teams with their subordinates, they build social capital that can have positive effects on team performance. Does this social capital affect team performance because subordinates come to see the leader as charismatic? We answered this question by examining 2 models. First, we tested the charisma-to-centrality model according to which the leader's charisma facilitates the occupation of a central position in the informal advice network. From this central position, the leader positively influences team performance. Second, we examined the centrality-to-charisma model according to which charisma is attributed to those leaders who are socially active in terms of giving and receiving advice. Attributed charisma facilitates increased team performance. We tested these 2 models in 2 different studies. In the first study, based on time-separated, multisource data emanating from members of 56 work teams, we found support for the centrality-to-charisma model. Formal leaders who were central within team advice networks were seen as charismatic by subordinates, and this charisma was associated with high team performance. To clarify how leader network centrality affected the emergence of charismatic leadership, we designed Study 2 in which, for 79 student teams, we measured leader networking activity and leader charisma at 2 different points in time and related these variables to team performance measured at a third point in time. On the basis of this temporally separated data set, we again found support for the centrality-to-charisma model.

  4. Visualizing Network Traffic to Understand the Performance of Massively Parallel Simulations.

    PubMed

    Landge, A G; Levine, J A; Bhatele, A; Isaacs, K E; Gamblin, T; Schulz, M; Langer, S H; Bremer, Peer-Timo; Pascucci, V

    2012-12-01

    The performance of massively parallel applications is often heavily impacted by the cost of communication among compute nodes. However, determining how to best use the network is a formidable task, made challenging by the ever increasing size and complexity of modern supercomputers. This paper applies visualization techniques to aid parallel application developers in understanding the network activity by enabling a detailed exploration of the flow of packets through the hardware interconnect. In order to visualize this large and complex data, we employ two linked views of the hardware network. The first is a 2D view, that represents the network structure as one of several simplified planar projections. This view is designed to allow a user to easily identify trends and patterns in the network traffic. The second is a 3D view that augments the 2D view by preserving the physical network topology and providing a context that is familiar to the application developers. Using the massively parallel multi-physics code pF3D as a case study, we demonstrate that our tool provides valuable insight that we use to explain and optimize pF3D's performance on an IBM Blue Gene/P system.

  5. Investigation of 3G radio network topologies considering performance and exposure aspects

    NASA Astrophysics Data System (ADS)

    Buddendick, H.; Wertz, P.; Eibert, T. F.; Landstorfer, F. M.; Wölfle, G.

    2007-06-01

    Five different radio network topologies, in the following referred to as "scenario", have been analyzed with regard to their capability to cover a medium size city with 3G mobile services. T-Mobile launched this study to assess different radio network topologies in terms of exposure. Thus, the main focus is to examine if, for exposure reasons, a medium size city can be covered with outer-city located sites. The radio network topologies discussed in this paper can generally be divided into two categories: In category one, the sites are located in the city, and in the other, the sites are located outside of the built-up area. In both cases the number of sites per scenario varies, and additionally the antenna height and site distance to the built-up area vary for the second category. The investigations are performed with a ray optical propagation model based on 3-D building data and a dynamic 3G network simulator. A mix of four services and a realistic spatial traffic distribution based on land usage is assumed. The performance of the scenarios, assessed by the evaluation of coverage and capacity, is examined together with the exposure. It is shown that network topologies with sites located only outside the city are not an adequate solution due to capacity constraints and coverage problems with high data services. A promising network topology is a reduced set of inner-city sites with higher order sectorization.

  6. Vector carpets

    SciTech Connect

    Dovey, D.

    1995-03-22

    Previous papers have described a general method for visualizing vector fields that involves drawing many small ``glyphs`` to represent the field. This paper shows how to improve the speed of the algorithm by utilizing hardware support for line drawing and extends the technique from regular to unstructured grids. The new approach can be used to visualize vector fields at arbitrary surfaces within regular and unstructured grids. Applications of the algorithm include interactive visualization of transient electromagnetic fields and visualization of velocity fields in fluid flow problems.

  7. Comparative performance of linear and nonlinear neural networks to predict irregular breathing.

    PubMed

    Murphy, Martin J; Dieterich, Sonja

    2006-11-21

    Breathing adaptation during external-beam radiotherapy is a matter of great concern because uncompensated tumour motion requires extended treatment margins that endanger sensitive tissue. Compensation strategies include beam gating, collimator tracking and robotic beam re-alignment. All of these schemes have a system latency of up to several hundred milliseconds, which calls in turn for predictive control loops. Irregularities in breathing make prediction difficult. We have evaluated the performance of two classes of control loop algorithms-the linear adaptive filter and the adaptive nonlinear neural network-for highly irregular patient breathing behaviours. The neural network demonstrated robust adaptability to all of the observed breathing patterns while the linear filter failed in a significant percentage of cases. For those cases where the linear filter could function, it made less accurate predictions than the neural network. Because the neural network presents no additional computational burden in the control loop we conclude that it is the preferred choice among heuristic predictive algorithms.

  8. Distinct Aging Effects on Functional Networks in Good and Poor Cognitive Performers

    PubMed Central

    Lee, Annie; Tan, Mingzhen; Qiu, Anqi

    2016-01-01

    Brain network hubs are susceptible to normal aging processes and disruptions of their functional connectivity are detrimental to decline in cognitive functions in older adults. However, it remains unclear how the functional connectivity of network hubs cope with cognitive heterogeneity in an aging population. This study utilized cognitive and resting-state functional magnetic resonance imaging data, cluster analysis, and graph network analysis to examine age-related alterations in the network hubs’ functional connectivity of good and poor cognitive performers. Our results revealed that poor cognitive performers showed age-dependent disruptions in the functional connectivity of the right insula and posterior cingulate cortex (PCC), while good cognitive performers showed age-related disruptions in the functional connectivity of the left insula and PCC. Additionally, the left PCC had age-related declines in the functional connectivity with the left medial prefrontal cortex (mPFC) and anterior cingulate cortex (ACC). Most interestingly, good cognitive performers showed age-related declines in the functional connectivity of the left insula and PCC with their right homotopic structures. These results may provide insights of neuronal correlates for understanding individual differences in aging. In particular, our study suggests prominent protection roles of the left insula and PCC and bilateral ACC in good performers. PMID:27667972

  9. Advanced Communication Technology Satellite (ACTS) Very Small Aperture Terminal (VSAT) Network Control Performance

    NASA Technical Reports Server (NTRS)

    Coney, T. A.

    1996-01-01

    This paper discusses the performance of the network control function for the Advanced Communications Technology Satellite (ACTS) very small aperture terminal (VSAT) full mesh network. This includes control of all operational activities such as acquisition, synchronization, timing and rain fade compensation as well as control of all communications activities such as on-demand integrated services (voice, video, and date) connects and disconnects Operations control is provided by an in-band orderwire carried in the baseboard processor (BBP) control burst, the orderwire burst, the reference burst, and the uplink traffic burst. Communication services are provided by demand assigned multiple access (DAMA) protocols. The ACTS implementation of DAMA protocols ensures both on-demand and integrated voice, video and data services. Communications services control is also provided by the in-band orderwire but uses only the reference burst and the uplink traffic burst. The performance of the ACTS network control functions have been successfully tested during on-orbit checkout and in various VSAT networks in day to day operations. This paper discusses the network operations and services control performance.

  10. Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?

    PubMed

    Balabin, Roman M; Lomakina, Ekaterina I

    2011-06-28

    A multilayer feed-forward artificial neural network (MLP-ANN) with a single, hidden layer that contains a finite number of neurons can be regarded as a universal non-linear approximator. Today, the ANN method and linear regression (MLR) model are widely used for quantum chemistry (QC) data analysis (e.g., thermochemistry) to improve their accuracy (e.g., Gaussian G2-G4, B3LYP/B3-LYP, X1, or W1 theoretical methods). In this study, an alternative approach based on support vector machines (SVMs) is used, the least squares support vector machine (LS-SVM) regression. It has been applied to ab initio (first principle) and density functional theory (DFT) quantum chemistry data. So, QC + SVM methodology is an alternative to QC + ANN one. The task of the study was to estimate the Møller-Plesset (MPn) or DFT (B3LYP, BLYP, BMK) energies calculated with large basis sets (e.g., 6-311G(3df,3pd)) using smaller ones (6-311G, 6-311G*, 6-311G**) plus molecular descriptors. A molecular set (BRM-208) containing a total of 208 organic molecules was constructed and used for the LS-SVM training, cross-validation, and testing. MP2, MP3, MP4(DQ), MP4(SDQ), and MP4/MP4(SDTQ) ab initio methods were tested. Hartree-Fock (HF/SCF) results were also reported for comparison. Furthermore, constitutional (CD: total number of atoms and mole fractions of different atoms) and quantum-chemical (QD: HOMO-LUMO gap, dipole moment, average polarizability, and quadrupole moment) molecular descriptors were used for the building of the LS-SVM calibration model. Prediction accuracies (MADs) of 1.62 ± 0.51 and 0.85 ± 0.24 kcal mol(-1) (1 kcal mol(-1) = 4.184 kJ mol(-1)) were reached for SVM-based approximations of ab initio and DFT energies, respectively. The LS-SVM model was more accurate than the MLR model. A comparison with the artificial neural network approach shows that the accuracy of the LS-SVM method is similar to the accuracy of ANN. The extrapolation and interpolation results show that LS-SVM is

  11. Structure and Topology Dynamics of Hyper-Frequency Networks during Rest and Auditory Oddball Performance.

    PubMed

    Müller, Viktor; Perdikis, Dionysios; von Oertzen, Timo; Sleimen-Malkoun, Rita; Jirsa, Viktor; Lindenberger, Ulman

    2016-01-01

    Resting-state and task-related recordings are characterized by oscillatory brain activity and widely distributed networks of synchronized oscillatory circuits. Electroencephalographic recordings (EEG) were used to assess network structure and network dynamics during resting state with eyes open and closed, and auditory oddball performance through phase synchronization between EEG channels. For this assessment, we constructed a hyper-frequency network (HFN) based on within- and cross-frequency coupling (WFC and CFC, respectively) at 10 oscillation frequencies ranging between 2 and 20 Hz. We found that CFC generally differentiates between task conditions better than WFC. CFC was the highest during resting state with eyes open. Using a graph-theoretical approach (GTA), we found that HFNs possess small-world network (SWN) topology with a slight tendency to random network characteristics. Moreover, analysis of the temporal fluctuations of HFNs revealed specific network topology dynamics (NTD), i.e., temporal changes of different graph-theoretical measures such as strength, clustering coefficient, characteristic path length (CPL), local, and global efficiency determined for HFNs at different time windows. The different topology metrics showed significant differences between conditions in the mean and standard deviation of these metrics both across time and nodes. In addition, using an artificial neural network approach, we found stimulus-related dynamics that varied across the different network topology metrics. We conclude that functional connectivity dynamics (FCD), or NTD, which was found using the HFN approach during rest and stimulus processing, reflects temporal and topological changes in the functional organization and reorganization of neuronal cell assemblies.

  12. Structure and Topology Dynamics of Hyper-Frequency Networks during Rest and Auditory Oddball Performance

    PubMed Central

    Müller, Viktor; Perdikis, Dionysios; von Oertzen, Timo; Sleimen-Malkoun, Rita; Jirsa, Viktor; Lindenberger, Ulman

    2016-01-01

    Resting-state and task-related recordings are characterized by oscillatory brain activity and widely distributed networks of synchronized oscillatory circuits. Electroencephalographic recordings (EEG) were used to assess network structure and network dynamics during resting state with eyes open and closed, and auditory oddball performance through phase synchronization between EEG channels. For this assessment, we constructed a hyper-frequency network (HFN) based on within- and cross-frequency coupling (WFC and CFC, respectively) at 10 oscillation frequencies ranging between 2 and 20 Hz. We found that CFC generally differentiates between task conditions better than WFC. CFC was the highest during resting state with eyes open. Using a graph-theoretical approach (GTA), we found that HFNs possess small-world network (SWN) topology with a slight tendency to random network characteristics. Moreover, analysis of the temporal fluctuations of HFNs revealed specific network topology dynamics (NTD), i.e., temporal changes of different graph-theoretical measures such as strength, clustering coefficient, characteristic path length (CPL), local, and global efficiency determined for HFNs at different time windows. The different topology metrics showed significant differences between conditions in the mean and standard deviation of these metrics both across time and nodes. In addition, using an artificial neural network approach, we found stimulus-related dynamics that varied across the different network topology metrics. We conclude that functional connectivity dynamics (FCD), or NTD, which was found using the HFN approach during rest and stimulus processing, reflects temporal and topological changes in the functional organization and reorganization of neuronal cell assemblies. PMID:27799906

  13. Support vector machine for automatic pain recognition

    NASA Astrophysics Data System (ADS)

    Monwar, Md Maruf; Rezaei, Siamak

    2009-02-01

    Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

  14. Combined networked switching output feedback control with ?-region stability for performance improvement

    NASA Astrophysics Data System (ADS)

    Nikolakopoulos, George; Dritsas, Leonidas; Delshad, Saleh S.

    2014-06-01

    In this article, a combined networked switching output feedback control scheme, with a ?-region stability performance improvement module is presented. The network induced time delays, that are considered to be time varying and integer multiples of the sampling period, are being embedded in the system model, by state augmentation. The resulting model of the overall networked closed-loop system is switching, with the current measured round-trip time delay acting as the switching rule. Based on this modelling approach, a Linear Matrix Inequality (LMI) tuned switching output feedback controller is designed. The proposed approach establishes robustness against time delays and is able to guarantee the overall stability of the switching closed-loop system. Integrated in the controlled synthesis phase, an LMI tuned performance improvement module is being introduced, based on ?-region stability. Multiple simulation results are being presented that prove the efficacy of the proposed scheme.

  15. System performance analysis of time-division-multiplexing passive optical network using directly modulated lasers or colorless optical network units

    NASA Astrophysics Data System (ADS)

    Gong, Xiaoxue; Guo, Lei; Liu, Yejun; Zhou, Yufang

    2015-05-01

    As a promising technology for broadband communication, passive optical network (PON) has been deployed to support the last-mile broadband access network. In particular, time-division-multiplexing PON (TDM-PON) has been widely used owing to its mature technology and low cost. To practically implement TDM-PONs, the combination of intensity modulation and direct detection is a very promising technique because it achieves cost reduction in system installation and maintenance. However, the current intensity-modulation and direct-detection TDM-PON still suffers from some problems, which mainly include a high-power penalty, detrimental Brillouin backscattering (BB), and so on. Thus, using directly modulated lasers (DMLs) and colorless optical network units (ONUs), respectively, two intensity-modulation and direct-detection TDM-PON architectures are proposed. Using VPI (an optical simulation software developed by VPIphotonics company) simulators, we first analyze the influences on DML-based intensity-modulation and direct-detection TDM-PON (system 1) performances, which mainly include bit error rate (BER) and power penalty. Next, the BB effect on the BER of the intensity-modulation and direct-detection TDM-PON that uses colorless ONUs (system 2) is also investigated. The simulation results show that: (1) a low-power penalty is achieved without degrading the BER of system 1, and (2) the BB can be effectively reduced using phase modulation of the optical carrier in system 2.

  16. Vector Magnetograph Design

    NASA Technical Reports Server (NTRS)

    Chipman, Russell A.

    1996-01-01

    This report covers work performed during the period of November 1994 through March 1996 on the design of a Space-borne Solar Vector Magnetograph. This work has been performed as part of a design team under the supervision of Dr. Mona Hagyard and Dr. Alan Gary of the Space Science Laboratory. Many tasks were performed and this report documents the results from some of those tasks, each contained in the corresponding appendix. Appendices are organized in chronological order.

  17. Data-flow Performance Optimisation on Unreliable Networks: the ATLAS Data-Acquisition Case

    NASA Astrophysics Data System (ADS)

    Colombo, Tommaso; ATLAS Collaboration

    2015-05-01

    The ATLAS detector at CERN records proton-proton collisions delivered by the Large Hadron Collider (LHC). The ATLAS Trigger and Data-Acquisition (TDAQ) system identifies, selects, and stores interesting collision data. These are received from the detector readout electronics at an average rate of 100 kHz. The typical event data size is 1 to 2 MB. Overall, the ATLAS TDAQ system can be seen as a distributed software system executed on a farm of roughly 2000 commodity PCs. The worker nodes are interconnected by an Ethernet network that at the restart of the LHC in 2015 is expected to experience a sustained throughput of several 10 GB/s. A particular type of challenge posed by this system, and by DAQ systems in general, is the inherently burstynature of the data traffic from the readout buffers to the worker nodes. This can cause instantaneous network congestion and therefore performance degradation. The effect is particularly pronounced for unreliable network interconnections, such as Ethernet. In this paper we report on the design of the data-flow software for the 2015-2018 data-taking period of the ATLAS experiment. This software will be responsible for transporting the data across the distributed Data-Acquisition system. We will focus on the strategies employed to manage the network congestion and therefore minimisethe data-collection latency and maximisethe system performance. We will discuss the results of systematic measurements performed on different types of networking hardware. These results highlight the causes of network congestion and the effects on the overall system performance.

  18. Prediction of SSVEP-based BCI performance by the resting-state EEG network

    NASA Astrophysics Data System (ADS)

    Zhang, Yangsong; Xu, Peng; Guo, Daqing; Yao, Dezhong

    2013-12-01

    Objective. The prediction of brain-computer interface (BCI) performance is a significant topic in the BCI field. Some researches have demonstrated that resting-state data are promising candidates to achieve the goal. However, so far the relationships between the resting-state networks and the steady-state visual evoked potential (SSVEP)-based BCI have not been investigated. In this paper, we investigate the possible relationships between the SSVEP responses, the classification accuracy of five stimulus frequencies and the closed-eye resting-state network topology. Approach. The resting-state functional connectivity networks of the corresponding five stimulus frequencies were created by coherence, and then three network topology measures—the mean functional connectivity, the clustering coefficient and the characteristic path length of each network—were calculated. In addition, canonical correlation analysis was used to perform frequency recognition with the SSVEP data. Main results. Interestingly, we found that SSVEPs of each frequency were negatively correlated with the mean functional connectivity and clustering coefficient, but positively correlated with characteristic path length. Each of the averaged network topology measures across the frequencies showed the same relationship with the SSVEPs averaged across frequencies between the subjects. Furthermore, our results also demonstrated that the classification accuracy can be predicted by three averaged network measures and their combination can further improve the prediction performance. Significance. These findings indicate that the SSVEP responses and performance are predictable using the information at the resting-state, which may be instructive in both SSVEP-aided cognition studies and SSVEP-based BCI applications.

  19. Towards a performance measurement system for health equity in a local health integration network.

    PubMed

    Nakaima, April; Sridharan, Sanjeev; Gardner, Bob

    2013-02-01

    While there is a growing literature on building performance measurement systems for health equities, this literature for the most part has not dealt with the challenges of coordinating the various parts of the system, the heterogeneous nature of such systems, or how evaluations and measurement can themselves improve performance. This paper describes the initial steps taken to build a performance measurement system to coordinate health equity across 18 hospitals led by the Toronto Central Local Health Integration Network, which is a regional health authority serving a population of more than 2.5 million residents (near in population to Chicago and Rome) and the most socially diverse urban network in Ontario, Canada. This paper also describes some principles that can help inform a performance measurement system. The innovative aspect of this paper is that these principles were developed through feedback by the hospitals.

  20. Optimal performance of networked control systems with bandwidth and coding constraints.

    PubMed

    Zhan, Xi-Sheng; Sun, Xin-xiang; Li, Tao; Wu, Jie; Jiang, Xiao-Wei

    2015-11-01

    The optimal tracking performance of multiple-input multiple-output (MIMO) discrete-time networked control systems with bandwidth and coding constraints is studied in this paper. The optimal tracking performance of networked control system is obtained by using spectral factorization technique and partial fraction. The obtained results demonstrate that the optimal performance is influenced by the directions and locations of the nonminimum phase zeros and unstable poles of the given plant. In addition to that, the characters of the reference signal, encoding, the bandwidth and additive white Gaussian noise (AWGN) of the communication channel are also closely influenced by the optimal tracking performance. Some typical examples are given to illustrate the theoretical results.

  1. Optimal modified tracking performance for networked control systems with QoS constraint.

    PubMed

    Zhan, Xi-Sheng; Sun, Xin-Xiang; Wu, Jie; Han, Tao

    2016-11-01

    This paper investigates the optimal modified tracking performance of networked control systems with a constraint on quality of service (QoS). The QoS is characterized by two parameters of the system, viz. data dropout and the additive white Gaussian noise. The proposed modified tracking performance index prevents the probability of invalid data arising from the variations in the tracking error in the absence of an integrator in the plant. The derived optimal filter eliminates the influence of channel noise in the feedback channel. The optimal modified tracking performance expression is obtained by using the co-prime factorization. Results indicate that the optimal modified tracking performance is influenced by the non-minimum phase zeros, modification factor, packet dropout probability, and the characteristics of the reference signals. The obtained results will give some guidance for the design of networked control systems. The efficiency of the model is verified using some typical examples.

  2. A Public-Private Partnership Improves Clinical Performance In A Hospital Network In Lesotho.

    PubMed

    McIntosh, Nathalie; Grabowski, Aria; Jack, Brian; Nkabane-Nkholongo, Elizabeth Limakatso; Vian, Taryn

    2015-06-01

    Health care public-private partnerships (PPPs) between a government and the private sector are based on a business model that aims to leverage private-sector expertise to improve clinical performance in hospitals and other health facilities. Although the financial implications of such partnerships have been analyzed, few studies have examined the partnerships' impact on clinical performance outcomes. Using quantitative measures that reflected capacity, utilization, clinical quality, and patient outcomes, we compared a government-managed hospital network in Lesotho, Africa, and the new PPP-managed hospital network that replaced it. In addition, we used key informant interviews to help explain differences in performance. We found that the PPP-managed network delivered more and higher-quality services and achieved significant gains in clinical outcomes, compared to the government-managed network. We conclude that health care public-private partnerships may improve hospital performance in developing countries and that changes in management and leadership practices might account for differences in clinical outcomes.

  3. Characterization on the performance of a fractal-shaped microchannel network for microelectronic cooling

    NASA Astrophysics Data System (ADS)

    Hong, F. J.; Cheng, P.; Wu, H. Y.

    2011-06-01

    Previous theoretical and analytical studies have shown that microchannel heat sinks with a fractal-shaped network have many advantages over traditional parallel microchannels with respect to thermal resistance, temperature uniformity and pressure drop. However, to the best knowledge of the authors, no experimental investigations on fractal-shaped microchannel network heat sinks have been conducted so far to verify their performance. In this paper, we designed and fabricated a silicon-based microchannel heat sink with a single-layered fractal-shaped microchannel network using MEMS technology, and experimentally studied its pressure drop and thermal resistance characteristics under different mass flow rate and heat flux conditions. Numerical simulations are performed to predict the heat sink performance under the same experimental conditions. It is found that the experimentally measured pressure drop in the heat sink has a nonlinear relationship with the mass flow rate, which agrees very well with the numerical simulation result. It is also found that the experimentally measured thermal resistance is also in reasonably good agreement with the numerical simulation, and therefore indirectly verifies the conclusion of previous numerical simulations that the performance of the fractal-shaped microchannel network is better than that of traditional parallel microchannels.

  4. Early detection monitoring of aquatic invasive species: Measuring performance success in a Lake Superior pilot network

    EPA Science Inventory

    The Great Lakes Water Quality Agreement, Annex 6 calls for a U.S.-Canada, basin-wide aquatic invasive species early detection network by 2015. The objective of our research is to explore survey design strategies that can improve detection efficiency, and to develop performance me...

  5. Performance of a random access packet network with time-capture capability

    NASA Astrophysics Data System (ADS)

    Lin, Y. H.

    The Joint Tactical Information Distribution System (JTIDS) is applied to a digital network supporting the command, control and communication requirements of 105 highly mobile users. User data traffic is bursty and the slotted ALOHA channel access scheme is therefore employed. This paper focuses on the determination of JTIDS system performance in this particular application. Emphasis is directed at the specific time-capture capability of JTIDS. Significant system performance parameters are quantified with analysis and simulation.

  6. Performance of a random access packet network with time-capture capability

    NASA Technical Reports Server (NTRS)

    Lin, Y. H.

    1983-01-01

    The Joint Tactical Information Distribution System (JTIDS) is applied to a digital network supporting the command, control and communication requirements of 105 highly mobile users. User data traffic is bursty and the slotted ALOHA channel access scheme is therefore employed. This paper focuses on the determination of JTIDS system performance in this particular application. Emphasis is directed at the specific time-capture capability of JTIDS. Significant system performance parameters are quantified with analysis and simulation.

  7. Convolutional Neural Network on Embedded Linux System-on-Chip: A Methodology and Performance Benchmark

    DTIC Science & Technology

    2016-05-01

    heat sink. Note that a final system could be made much smaller than this development board, which has “wasted” space compared to a board used in a...TECHNICAL REPORT 3010 May 2016 Convolutional Neural Network on Embedded Linux® System -on-Chip A Methodology and Performance Benchmark Daniel...in this report was performed by the IO Support to National Security Branch (Code 56120), the Mission Systems Engineering Branch (Code 56170), and the

  8. [Buruli ulcer: a dynamic transversal research model performed through the international network of Pasteur Institutes].

    PubMed

    Marion, Estelle; Landier, Jordi; Eyangoh, Sara; Marsollier, Laurent

    2013-10-01

    Buruli ulcer is an endemic severe human skin disease caused by Mycobacterium ulcerans, which prevails in western Africa in swampy areas and primarily hits children. Its gravity comes from the extent of tissue destruction, created by the toxin mycolactone. We describe here how the Centre Pasteur of Cameroon, with the help of the ministry of Health, gathered a network of multidisciplinary partners to fight against Buruli ulcer starting in the years 2000. The Centre Pasteur develops three missions : patient care, training of health care workers and research on the insect vector. Ten years of efforts resulted in significant medical advances such as the design of an early diagnostic test using PCR, or the observation that bed net use significantly decreased the risk of Buruli ulcer, offering useful prevention ; on the research side, entomological studies on aquatic bugs, coupled with epidemiological data, point to the role of these insects in the transmission of the disease. This study examplifies how an efficient network can contribute to the prevention and treatment of debilitating infectious diseases.

  9. A Case Study of Performance Degradation Attributable to Run-Time Bounds Checks on C++ Vector Access

    PubMed Central

    Flater, David; Guthrie, William F

    2013-01-01

    Programmers routinely omit run-time safety checks from applications because they assume that these safety checks would degrade performance. The simplest example is the use of arrays or array-like data structures that do not enforce the constraint that indices must be within bounds. This report documents an attempt to measure the performance penalty incurred by two different implementations of bounds-checking in C and C++ using a simple benchmark and a desktop PC with a modern superscalar CPU. The benchmark consisted of a loop that wrote to array elements in sequential order. With this configuration, relative to the best performance observed for any access method in C or C++, mean degradation of only (0.881 ± 0.009) % was measured for a standard bounds-checking access method in C++. This case study showed the need for further work to develop and refine measurement methods and to perform more comparisons of this type. Comparisons across different use cases, configurations, programming languages, and environments are needed to determine under what circumstances (if any) the performance advantage of unchecked access is actually sufficient to outweigh the negative consequences for security and software quality. PMID:26401432

  10. Real-time performance analysis of wireless multimedia networks based on partially observed multivariate point processes

    NASA Astrophysics Data System (ADS)

    Hortos, William S.

    2000-07-01

    Third-generation (3G) wireless networks will support integrated multimedia services based on a cellular extension of a packet-switched architecture using variants of the Internet protocol (IP). Services can be categorized as real- time and delay-sensitive, or non-real-time and delay- insensitive. Each call, arriving to or active within the network, carries demand for one or more services in parallel; each service type with a guaranteed quality of service (QoS). Admission of new calls to the wireless IP network (WIN) from the gateway of a wired network or from a mobile subscriber (MS) is allowed by call admission control procedures. Roaming of the MSs among the nodes of the WIN is controlled by handoff procedures between base stations (BSs), or BS controllers, and the MSs. Metrics such as the probabilities of call blocking and dropping, handoff transition time, processing latency of a call, throughput, and capacity are used to evaluate the performance of network control procedures. The metrics are directly related to the network resources required to provide the QoS for the integrated services.

  11. Changes in brain network efficiency and working memory performance in aging.

    PubMed

    Stanley, Matthew L; Simpson, Sean L; Dagenbach, Dale; Lyday, Robert G; Burdette, Jonathan H; Laurienti, Paul J

    2015-01-01

    Working memory is a complex psychological construct referring to the temporary storage and active processing of information. We used functional connectivity brain network metrics quantifying local and global efficiency of information transfer for predicting individual variability in working memory performance on an n-back task in both young (n = 14) and older (n = 15) adults. Individual differences in both local and global efficiency during the working memory task were significant predictors of working memory performance in addition to age (and an interaction between age and global efficiency). Decreases in local efficiency during the working memory task were associated with better working memory performance in both age cohorts. In contrast, increases in global efficiency were associated with much better working performance for young participants; however, increases in global efficiency were associated with a slight decrease in working memory performance for older participants. Individual differences in local and global efficiency during resting-state sessions were not significant predictors of working memory performance. Significant group whole-brain functional network decreases in local efficiency also were observed during the working memory task compared to rest, whereas no significant differences were observed in network global efficiency. These results are discussed in relation to recently developed models of age-related differences in working memory.

  12. Architecture Modeling and Performance Characterization of Space Communications and Navigation (SCaN) Network Using MACHETE

    NASA Technical Reports Server (NTRS)

    Jennings, Esther; Heckman, David

    2008-01-01

    As future space exploration missions will involve larger number of spacecraft and more complex systems, theoretical analysis alone may have limitations on characterizing system performance and interactions among the systems. Simulation tools can be useful for system performance characterization through detailed modeling and simulation of the systems and its environment...This paper reports the simulation of the Orion (Crew Exploration Vehicle) to the International Space Station (ISS) mission where Orion is launched by Ares into orbit on a 14-day mission to rendezvous with the ISS. Communications services for the mission are provided by the Space Communication and Navigation (SCaN) network infrastructure which includes the NASA Space Network (SN), Ground Network (GN) and NASA Integrated Services Network (NISN). The objectives of the simulation are to determine whether SCaN can meet the communications needs of the mission, to demonstrate the benefit of using QoS prioritization, and to evaluate network-key parameters of interest such as delay and throughout.

  13. Study of Synthetic Vision Systems (SVS) and Velocity-vector Based Command Augmentation System (V-CAS) on Pilot Performance

    NASA Technical Reports Server (NTRS)

    Liu, Dahai; Goodric, Ken; Peak, Bob

    2006-01-01

    This study investigated the effects of synthetic vision system (SVS) concepts and advanced flight controls on single pilot performance (SPP). Specifically, we evaluated the benefits and interactions of two levels of terrain portrayal, guidance symbology, and control-system response type on SPP in the context of lower-landing minima (LLM) approaches. Performance measures consisted of flight technical error (FTE) and pilot perceived workload. In this study, pilot rating, control type, and guidance symbology were not found to significantly affect FTE or workload. It is likely that transfer from prior experience, limited scope of the evaluation task, specific implementation limitations, and limited sample size were major factors in obtaining these results.

  14. Performance of twin two-dimensional wedge nozzles including thrust vectoring and reversing effects at speeds up to Mach 2.20

    NASA Technical Reports Server (NTRS)

    Capone, F. J.; Maiden, D. L.

    1977-01-01

    Transonic tunnel and supersonic pressure tunnel tests were reformed to determine the performance characteristics of twin nonaxisymmetric or two-dimensional nozzles with fixed shrouds and variable-geometry wedges. The effects of thrust vectoring, reversing, and installation of various tails were also studied. The investigation was conducted statically and at flight speeds up to a Mach number of 2.20. The total pressure ratio of the simulated jet exhaust was varied up to approximately 26 depending on Mach number. The Reynolds number per meter varied up to 13.20 x 1 million. An analytical study was made to determine the effect on calculated wave drag by varying the mathematical model used to simulate nozzle jet-exhaust plume.

  15. Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks.

    PubMed

    Martens, Marijn B; Houweling, Arthur R; E Tiesinga, Paul H

    2017-02-01

    Neuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the underlying neural networks provide stability against internal fluctuations in the firing rate, while simultaneously making the circuits sensitive to small external perturbations. Here we studied whether stability and sensitivity are affected by the connectivity structure in recurrently connected spiking networks. We found that anti-correlation between the number of afferent (in-degree) and efferent (out-degree) synaptic connections of neurons increases stability against pathological bursting, relative to networks where the degrees were either positively correlated or uncorrelated. In the stable network state, stimulation of a few cells could lead to a detectable change in the firing rate. To quantify the ability of networks to detect the stimulation, we used a receiver operating characteristic (ROC) analysis. For a given level of background noise, networks with anti-correlated degrees displayed the lowest false positive rates, and consequently had the highest stimulus detection performance. We propose that anti-correlation in the degree distribution may be a computational strategy employed by sensory cortices to increase the detectability of external stimuli. We show that networks with anti-correlated degrees can in principle be formed by applying learning rules comprised of a combination of spike-timing dependent plasticity, homeostatic plasticity and pruning to networks with uncorrelated degrees. To test our prediction we suggest a novel experimental method to estimate correlations in the degree distribution.

  16. Co-scheduling of network resource provisioning and host-to-host bandwidth reservation on high-performance network and storage systems

    SciTech Connect

    Yu, Dantong; Katramatos, Dimitrios; Sim, Alexander; Shoshani, Arie

    2014-04-22

    A cross-domain network resource reservation scheduler configured to schedule a path from at least one end-site includes a management plane device configured to monitor and provide information representing at least one of functionality, performance, faults, and fault recovery associated with a network resource; a control plane device configured to at least one of schedule the network resource, provision local area network quality of service, provision local area network bandwidth, and provision wide area network bandwidth; and a service plane device configured to interface with the control plane device to reserve the network resource based on a reservation request and the information from the management plane device. Corresponding methods and computer-readable medium are also disclosed.

  17. A three-dimensional carbon nano-network for high performance lithium ion batteries

    DOE PAGES

    Tian, Miao; Wang, Wei; Liu, Yang; ...

    2014-11-20

    Three-dimensional (3D) network structure has been envisioned as a superior architecture for lithium ion battery (LIB) electrodes, which enhances both ion and electron transport to significantly improve battery performance. Herein, a 3D carbon nano-network is fabricated through chemical vapor deposition of carbon on a scalably manufactured 3D porous anodic alumina (PAA) template. As a demonstration on the applicability of 3D carbon nano-network for LIB electrodes, the low conductivity active material, TiO2, is then uniformly coated on the 3D carbon nano-network using atomic layer deposition. High power performance is demonstrated in the 3D C/TiO2 electrodes, where the parallel tubes and gapsmore » in the 3D carbon nano-network facilitates fast Li ion transport. A large areal capacity of ~0.37 mAh·cm–2 is achieved due to the large TiO2 mass loading in the 60 µm-thick 3D C/TiO2 electrodes. At a test rate of C/5, the 3D C/TiO2 electrode with 18 nm-thick TiO2 delivers a high gravimetric capacity of ~240 mAh g–1, calculated with the mass of the whole electrode. A long cycle life of over 1000 cycles with a capacity retention of 91% is demonstrated at 1C. In this study, the effects of the electrical conductivity of carbon nano-network, ion diffusion, and the electrolyte permeability on the rate performance of these 3D C/TiO2 electrodes are systematically studied.« less

  18. A three-dimensional carbon nano-network for high performance lithium ion batteries

    SciTech Connect

    Tian, Miao; Wang, Wei; Liu, Yang; Jungjohann, Katherine L.; Thomas Harris, C.; Lee, Yung -Cheng; Yang, Ronggui

    2014-11-20

    Three-dimensional (3D) network structure has been envisioned as a superior architecture for lithium ion battery (LIB) electrodes, which enhances both ion and electron transport to significantly improve battery performance. Herein, a 3D carbon nano-network is fabricated through chemical vapor deposition of carbon on a scalably manufactured 3D porous anodic alumina (PAA) template. As a demonstration on the applicability of 3D carbon nano-network for LIB electrodes, the low conductivity active material, TiO2, is then uniformly coated on the 3D carbon nano-network using atomic layer deposition. High power performance is demonstrated in the 3D C/TiO2 electrodes, where the parallel tubes and gaps in the 3D carbon nano-network facilitates fast Li ion transport. A large areal capacity of ~0.37 mAh·cm–2 is achieved due to the large TiO2 mass loading in the 60 µm-thick 3D C/TiO2 electrodes. At a test rate of C/5, the 3D C/TiO2 electrode with 18 nm-thick TiO2 delivers a high gravimetric capacity of ~240 mAh g–1, calculated with the mass of the whole electrode. A long cycle life of over 1000 cycles with a capacity retention of 91% is demonstrated at 1C. In this study, the effects of the electrical conductivity of carbon nano-network, ion diffusion, and the electrolyte permeability on the rate performance of these 3D C/TiO2 electrodes are systematically studied.

  19. A framework for performance measurement in university using extended network data envelopment analysis (DEA) structures

    NASA Astrophysics Data System (ADS)

    Kashim, Rosmaini; Kasim, Maznah Mat; Rahman, Rosshairy Abd

    2015-12-01

    Measuring university performance is essential for efficient allocation and utilization of educational resources. In most of the previous studies, performance measurement in universities emphasized the operational efficiency and resource utilization without investigating the university's ability to fulfill the needs of its stakeholders and society. Therefore, assessment of the performance of university should be separated into two stages namely efficiency and effectiveness. In conventional DEA analysis, a decision making unit (DMU) or in this context, a university is generally treated as a black-box which ignores the operation and interdependence of the internal processes. When this happens, the results obtained would be misleading. Thus, this paper suggest an alternative framework for measuring the overall performance of a university by incorporating both efficiency and effectiveness and applies network DEA model. The network DEA models are recommended because this approach takes into account the interrelationship between the processes of efficiency and effectiveness in the system. This framework also focuses on the university structure which is expanded from the hierarchical to form a series of horizontal relationship between subordinate units by assuming both intermediate unit and its subordinate units can generate output(s). Three conceptual models are proposed to evaluate the performance of a university. An efficiency model is developed at the first stage by using hierarchical network model. It is followed by an effectiveness model which take output(s) from the hierarchical structure at the first stage as a input(s) at the second stage. As a result, a new overall performance model is proposed by combining both efficiency and effectiveness models. Thus, once this overall model is realized and utilized, the university's top management can determine the overall performance of each unit more accurately and systematically. Besides that, the result from the network

  20. Performance Analysis of the AeroTP Transport Protocol for Highly-Dynamic Airborne Telemetry Networks

    DTIC Science & Technology

    2011-06-03

    Acknowledgment Options.” RFC 2018 (Proposed Standard ), Oct. 1996. [11] “The ns- 3 network simulator.” http://www.nsnam.org, July 2009. [12] M. AL-Enazi, S. A. Gogi...AFFTC-PA- 11146 Performance Analysis of the AeroTP Transport Protocol for Highly-Dynamic Airborne Telemetry Networks James P.G. Sterbenz...Kamakshi Sirisha Pathapati, Truc Anh N. Nguyen, Justin P. Rohrer AIR FORCE FLIGHT TEST CENTER EDWARDS AFB, CA JUNE 3 , 2011 A F F T C

  1. Application of artificial neural network for prediction of marine diesel engine performance

    NASA Astrophysics Data System (ADS)

    Mohd Noor, C. W.; Mamat, R.; Najafi, G.; Nik, W. B. Wan; Fadhil, M.

    2015-12-01

    This study deals with an artificial neural network (ANN) modelling of a marine diesel engine to predict the brake power, output torque, brake specific fuel consumption, brake thermal efficiency and volumetric efficiency. The input data for network training was gathered from engine laboratory testing running at various engine speed. The prediction model was developed based on standard back-propagation Levenberg-Marquardt training algorithm. The performance of the model was validated by comparing the prediction data sets with the measured experiment data. Results showed that the ANN model provided good agreement with the experimental data with high accuracy.

  2. Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance.

    PubMed

    Violante, Ines R; Li, Lucia M; Carmichael, David W; Lorenz, Romy; Leech, Robert; Hampshire, Adam; Rothwell, John C; Sharp, David J

    2017-03-14

    Cognitive functions such as working memory (WM) are emergent properties of large-scale network interactions. Synchronisation of oscillatory activity might contribute to WM by enabling the coordination of long-range processes. However, causal evidence for the way oscillatory activity shapes network dynamics and behavior in humans is limited. Here we applied transcranial alternating current stimulation (tACS) to exogenously modulate oscillatory activity in a right frontoparietal network that supports WM. Externally induced synchronization improved performance when cognitive demands were high. Simultaneously collected fMRI data reveals tACS effects dependent on the relative phase of the stimulation and the internal cognitive processing state. Specifically, synchronous tACS during the verbal WM task increased parietal activity, which correlated with behavioral performance. Furthermore, functional connectivity results indicate that the relative phase of frontoparietal stimulation influences information flow within the WM network. Overall, our findings demonstrate a link between behavioral performance in a demanding WM task and large-scale brain synchronization.

  3. 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.

  4. Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance

    PubMed Central

    Violante, Ines R; Li, Lucia M; Carmichael, David W; Lorenz, Romy; Leech, Robert; Hampshire, Adam; Rothwell, John C; Sharp, David J

    2017-01-01

    Cognitive functions such as working memory (WM) are emergent properties of large-scale network interactions. Synchronisation of oscillatory activity might contribute to WM by enabling the coordination of long-range processes. However, causal evidence for the way oscillatory activity shapes network dynamics and behavior in humans is limited. Here we applied transcranial alternating current stimulation (tACS) to exogenously modulate oscillatory activity in a right frontoparietal network that supports WM. Externally induced synchronization improved performance when cognitive demands were high. Simultaneously collected fMRI data reveals tACS effects dependent on the relative phase of the stimulation and the internal cognitive processing state. Specifically, synchronous tACS during the verbal WM task increased parietal activity, which correlated with behavioral performance. Furthermore, functional connectivity results indicate that the relative phase of frontoparietal stimulation influences information flow within the WM network. Overall, our findings demonstrate a link between behavioral performance in a demanding WM task and large-scale brain synchronization. DOI: http://dx.doi.org/10.7554/eLife.22001.001 PMID:28288700

  5. Static and dynamic posterior cingulate cortex nodal topology of default mode network predicts attention task performance.

    PubMed

    Lin, Pan; Yang, Yong; Jovicich, Jorge; De Pisapia, Nicola; Wang, Xiang; Zuo, Chun S; Levitt, James Jonathan

    2016-03-01

    Characterization of the default mode network (DMN) as a complex network of functionally interacting dynamic systems has received great interest for the study of DMN neural mechanisms. In particular, understanding the relationship of intrinsic resting-state DMN brain network with cognitive behaviors is an important issue in healthy cognition and mental disorders. However, it is still unclear how DMN functional connectivity links to cognitive behaviors during resting-state. In this study, we hypothesize that static and dynamic DMN nodal topology is associated with upcoming cognitive task performance. We used graph theory analysis in order to understand better the relationship between the DMN functional connectivity and cognitive behavior during resting-state and task performance. Nodal degree of the DMN was calculated as a metric of network topology. We found that the static and dynamic posterior cingulate cortex (PCC) nodal degree within the DMN was associated with task performance (Reaction Time). Our results show that the core node PCC nodal degree within the DMN was significantly correlated with reaction time, which suggests that the PCC plays a key role in supporting cognitive function.

  6. Dual Arm Work Package performance estimates and telerobot task network simulation

    SciTech Connect

    Draper, J.V.; Blair, L.M.

    1997-02-01

    This paper describes the methodology and results of a network simulation study of the Dual Arm Work Package (DAWP), to be employed for dismantling the Argonne National Laboratory CP-5 reactor. The development of the simulation model was based upon the results of a task analysis for the same system. This study was performed by the Oak Ridge National Laboratory (ORNL), in the Robotics and Process Systems Division. Funding was provided the US Department of Energy`s Office of Technology Development, Robotics Technology Development Program (RTDP). The RTDP is developing methods of computer simulation to estimate telerobotic system performance. Data were collected to provide point estimates to be used in a task network simulation model. Three skilled operators performed six repetitions of a pipe cutting task representative of typical teleoperation cutting operations.

  7. Radiation exposure effects on the performance of an electrically trainable artificial neural network (ETANN)

    SciTech Connect

    Castro, H.A. ); Sweet, M.R. )

    1993-12-01

    The authors present the effects of radiation exposure on an analog neural network device. The neural network implements a fully parallel architecture integrating 10,240 analog non-volatile synapses fabricated in a CMOS process. Graceful degradation of forward propagation performance analog non-volatile synapses fabricated in a CMOS process. Graceful degradation of forward propagation performance was observed in units that were exposed to absorbed doses of up to 26 Krads (Si) of 10 MeV electrons. The units were exposed without bias, except for that due to the floating gates. Single chip solutions to two pattern recognition problems representing two levels of difficulty are employed for testing. Post-irradiation-effects are observed over the following weeks after exposure due to latent charge trapping mechanism in the oxides of the non-volatile floating gate structures. They show that with the suitable algorithm and model, units with apparently permanent damage can be retrained to 100% recognition performance.

  8. Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

    PubMed Central

    Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

    2017-01-01

    Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm. PMID:28257060

  9. Prediction of temperature performance of a two-phase closed thermosyphon using Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Shanbedi, Mehdi; Jafari, Dariush; Amiri, Ahmad; Heris, Saeed Zeinali; Baniadam, Majid

    2013-01-01

    Here, the temperature performance of a two-phase closed thermosyphon (TPCT) was investigated using two synthesized nanofluids, including carbon nano-tube (CNT)/water and CNT-Ag/water. In order to determine the temperature performance of a TPCT, the experiments were performed for various values of weight fraction and input power. To predict the other experimental conditions, a reliable and accurate tool should be applied. Therefore Artificial Neural Network (ANN) was applied to predict the process performance. Using ANN, the operating parameters, including distribution of wall temperature (T) and the temperature difference between the input and the output water streams of condenser section (∆T) were determined. To achieve this goal, the multi-layer perceptron network was employed. The Levenberg-Marquardt algorithm was chosen as learning algorithm of this network. The results of simulation showed an excellent agreement with the data resulted from the experiments. Therefore it is possible to say that ANN is a powerful tool to predict the performance of different processes.

  10. Personal and Network Dynamics in Performance of Knowledge Workers: A Study of Australian Breast Radiologists

    PubMed Central

    Tavakoli Taba, Seyedamir; Hossain, Liaquat; Heard, Robert; Brennan, Patrick; Lee, Warwick; Lewis, Sarah

    2016-01-01

    Materials and Methods In this paper, we propose a theoretical model based upon previous studies about personal and social network dynamics of job performance. We provide empirical support for this model using real-world data within the context of the Australian radiology profession. An examination of radiologists’ professional network topology through structural-positional and relational dimensions and radiologists’ personal characteristics in terms of knowledge, experience and self-esteem is provided. Thirty one breast imaging radiologists completed a purpose designed questionnaire regarding their network characteristics and personal attributes. These radiologists also independently read a test set of 60 mammographic cases: 20 cases with cancer and 40 normal cases. A Jackknife free response operating characteristic (JAFROC) method was used to measure the performance of the radiologists’ in detecting breast cancers. Results Correlational analyses showed that reader performance was positively correlated with the social network variables of degree centrality and effective size, but negatively correlated with constraint and hierarchy. For personal characteristics, the number of mammograms read per year and self-esteem (self-evaluation) positively correlated with reader performance. Hierarchical multiple regression analysis indicated that the combination of number of mammograms read per year and network’s effective size, hierarchy and tie strength was the best fitting model, explaining 63.4% of the variance in reader performance. The results from this study indicate the positive relationship between reading high volumes of cases by radiologists and expertise development, but also strongly emphasise the association between effective social/professional interactions and informal knowledge sharing with high performance. PMID:26918644

  11. 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.

  12. 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.

  13. Predicting the performance of local seismic networks using Matlab and Google Earth.

    SciTech Connect

    Chael, Eric Paul

    2009-11-01

    We have used Matlab and Google Earth to construct a prototype application for modeling the performance of local seismic networks for monitoring small, contained explosions. Published equations based on refraction experiments provide estimates of peak ground velocities as a function of event distance and charge weight. Matlab routines implement these relations to calculate the amplitudes across a network of stations from sources distributed over a geographic grid. The amplitudes are then compared to ambient noise levels at the stations, and scaled to determine the smallest yield that could be detected at each source location by a specified minimum number of stations. We use Google Earth as the primary user interface, both for positioning the stations of a hypothetical local network, and for displaying the resulting detection threshold contours.

  14. Designing optimal greenhouse gas observing networks that consider performance and cost

    DOE PAGES

    Lucas, D. D.; Yver Kwok, C.; Cameron-Smith, P.; ...

    2015-06-16

    Emission rates of greenhouse gases (GHGs) entering into the atmosphere can be inferred using mathematical inverse approaches that combine observations from a network of stations with forward atmospheric transport models. Some locations for collecting observations are better than others for constraining GHG emissions through the inversion, but the best locations for the inversion may be inaccessible or limited by economic and other non-scientific factors. We present a method to design an optimal GHG observing network in the presence of multiple objectives that may be in conflict with each other. As a demonstration, we use our method to design a prototypemore » network of six stations to monitor summertime emissions in California of the potent GHG 1,1,1,2-tetrafluoroethane (CH2FCF3, HFC-134a). We use a multiobjective genetic algorithm to evolve network configurations that seek to jointly maximize the scientific accuracy of the inferred HFC-134a emissions and minimize the associated costs of making the measurements. The genetic algorithm effectively determines a set of "optimal" observing networks for HFC-134a that satisfy both objectives (i.e., the Pareto frontier). The Pareto frontier is convex, and clearly shows the tradeoffs between performance and cost, and the diminishing returns in trading one for the other. Without difficulty, our method can be extended to design optimal networks to monitor two or more GHGs with different emissions patterns, or to incorporate other objectives and constraints that are important in the practical design of atmospheric monitoring networks.« less

  15. A New Approach in Advance Network Reservation and Provisioning for High-Performance Scientific Data Transfers

    SciTech Connect

    Balman, Mehmet; Chaniotakis, Evangelos; Shoshani, Arie; Sim, Alex

    2010-01-28

    Scientific applications already generate many terabytes and even petabytes of data from supercomputer runs and large-scale experiments. The need for transferring data chunks of ever-increasing sizes through the network shows no sign of abating. Hence, we need high-bandwidth high speed networks such as ESnet (Energy Sciences Network). Network reservation systems, i.e. ESnet's OSCARS (On-demand Secure Circuits and Advance Reservation System) establish guaranteed bandwidth of secure virtual circuits at a certain time, for a certain bandwidth and length of time. OSCARS checks network availability and capacity for the specified period of time, and allocates requested bandwidth for that user if it is available. If the requested reservation cannot be granted, no further suggestion is returned back to the user. Further, there is no possibility from the users view-point to make an optimal choice. We report a new algorithm, where the user specifies the total volume that needs to be transferred, a maximum bandwidth that he/she can use, and a desired time period within which the transfer should be done. The algorithm can find alternate allocation possibilities, including earliest time for completion, or shortest transfer duration - leaving the choice to the user. We present a novel approach for path finding in time-dependent networks, and a new polynomial algorithm to find possible reservation options according to given constraints. We have implemented our algorithm for testing and incorporation into a future version of ESnet?s OSCARS. Our approach provides a basis for provisioning end-to-end high performance data transfers over storage and network resources.

  16. High performances CNTFETs achieved using CNT networks for selective gas sensing

    NASA Astrophysics Data System (ADS)

    Gorintin, Louis; Bondavalli, Paolo; Legagneux, Pierre; Pribat, Didier

    2009-08-01

    Our study deals with the utilization of carbon nanotubes networks based transistors with different metal electrodes for highly selective gas sensing. Indeed, carbon nanotubes networks can be used as semi conducting materials to achieve good performances transistors. These devices are extremely sensitive to the change of the Schottky barrier heights between Single Wall Carbon Nanotubes (SWCNTs) and drain/source metal electrodes: the gas adsorption creates an interfacial dipole that modifies the metal work function and so the bending and the height of the Schottky barrier at the contacts. Moreover each gas interacts specifically with each metal identifying a sort of electronic fingerprinting. Using airbrush technique for deposition, we have been able to achieve uniform random networks of carbon nanotubes suitable for large area applications and mass production such as fabrication of CNT based gas sensors. These networks enable us to achieve transistors with on/off ratio of more than 5 orders of magnitude. To reach these characteristics, the density of the CNT network has been adjusted in order to reach the percolation threshold only for semi-conducting nanotubes. These optimized devices have allowed us to tune the sensitivity (improving it) of our sensors for highly selective detection of DiMethyl-Methyl-Phosphonate (DMMP, a sarin stimulant), and even volatile drug precursors using Pd, Au and Mo electrodes.

  17. A Workflow-based Intelligent Network Data Movement Advisor with End-to-end Performance Optimization

    SciTech Connect

    Zhu, Michelle M.; Wu, Chase Q.

    2013-11-07

    Next-generation eScience applications often generate large amounts of simulation, experimental, or observational data that must be shared and managed by collaborative organizations. Advanced networking technologies and services have been rapidly developed and deployed to facilitate such massive data transfer. However, these technologies and services have not been fully utilized mainly because their use typically requires significant domain knowledge and in many cases application users are even not aware of their existence. By leveraging the functionalities of an existing Network-Aware Data Movement Advisor (NADMA) utility, we propose a new Workflow-based Intelligent Network Data Movement Advisor (WINDMA) with end-to-end performance optimization for this DOE funded project. This WINDMA system integrates three major components: resource discovery, data movement, and status monitoring, and supports the sharing of common data movement workflows through account and database management. This system provides a web interface and interacts with existing data/space management and discovery services such as Storage Resource Management, transport methods such as GridFTP and GlobusOnline, and network resource provisioning brokers such as ION and OSCARS. We demonstrate the efficacy of the proposed transport-support workflow system in several use cases based on its implementation and deployment in DOE wide-area networks.

  18. Performance limitations of a free-space optical communication satellite network owing to vibrations: heterodyne detection.

    PubMed

    Arnon, S; Rotman, S R; Kopeika, N S

    1998-09-20

    Free-space optical communication between satellites in a distributed network can permit high data rates of communication between different places on Earth. To establish optical communication between any two satellites requires that the line of sight of their optics be aligned during the entire communication time. Because of the large distance between the satellites and the alignment accuracy required, the pointing from one satellite to another is complicated because of vibrations of the pointing system caused by two fundamental stochastic mechanisms: tracking noise created by the electro-optic tracker and vibrations derived from mechanical components. Vibration of the transmitter beam in the receiver plane causes a decrease in the received optical power. Vibrations of the receiver telescope relative to the received beam decrease the heterodyne mixing efficiency. These two factors increase the bit-error rate of a coherent detection network. We derive simple mathematical models of the network bit-error rate versus the system parameters and the transmitter and receiver vibration statistics. An example of a practical optical heterodyne free-space satellite optical communication network is presented. From this research it is clear that even low-amplitude vibration of the satellite-pointing systems dramatically decreases network performance.

  19. Performance evaluation of an importance sampling technique in a Jackson network

    NASA Astrophysics Data System (ADS)

    brahim Mahdipour, E.; Masoud Rahmani, Amir; Setayeshi, Saeed

    2014-03-01

    Importance sampling is a technique that is commonly used to speed up Monte Carlo simulation of rare events. However, little is known regarding the design of efficient importance sampling algorithms in the context of queueing networks. The standard approach, which simulates the system using an a priori fixed change of measure suggested by large deviation analysis, has been shown to fail in even the simplest network settings. Estimating probabilities associated with rare events has been a topic of great importance in queueing theory, and in applied probability at large. In this article, we analyse the performance of an importance sampling estimator for a rare event probability in a Jackson network. This article carries out strict deadlines to a two-node Jackson network with feedback whose arrival and service rates are modulated by an exogenous finite state Markov process. We have estimated the probability of network blocking for various sets of parameters, and also the probability of missing the deadline of customers for different loads and deadlines. We have finally shown that the probability of total population overflow may be affected by various deadline values, service rates and arrival rates.

  20. Structure-property relationship for in vitro siRNA delivery performance of cationic 2-hydroxypropyl-β-cyclodextrin: PEG-PPG-PEG polyrotaxane vectors.

    PubMed

    Badwaik, Vivek D; Aicart, Emilio; Mondjinou, Yawo A; Johnson, Merrell A; Bowman, Valorie D; Thompson, David H

    2016-04-01

    Nanoparticle-mediated siRNA delivery is a promising therapeutic approach, however, the processes required for transport of these materials across the numerous extracellular and intracellular barriers are poorly understood. Efficient delivery of siRNA-containing nanoparticles would ultimately benefit from an improved understanding of how parameters associated with these barriers relate to the physicochemical properties of the nanoparticle vectors. We report the synthesis of three Pluronic(®)-based, cholesterol end-capped cationic polyrotaxanes (PR(+)) threaded with 2-hydroxypropyl-β-cyclodextrin (HPβCD) for siRNA delivery. The biological data showed that PR(+):siRNA complexes were well tolerated (∼90% cell viability) and produced efficient silencing (>80%) in HeLa-GFP and NIH 3T3-GFP cell lines. We further used a multi-parametric approach to identify relationships between the PR(+) structure, PR(+):siRNA complex physical properties, and biological activity. Small angle X-ray scattering and cryoelectron microscopy studies reveal periodicity and lamellar architectures for PR(+):siRNA complexes, whereas the biological assays, ζ potential measurements, and imaging studies suggest that silencing efficiency is influenced by the effective charge ratio (ρeff), polypropylene oxide (PO) block length, and central PO block coverage (i.e., rigidity) of the PR(+) core. We infer from our findings that more compact PR(+):siRNA nanostructures arising from lower molecular weight, rigid rod-like PR(+) polymer cores produce improved silencing efficiency relative to higher molecular weight, more flexible PR(+) vectors of similar effective charge. This study demonstrates that PR(+):siRNA complex formulations can be produced having higher performance than Lipofectamine(®) 2000, while maintaining good cell viability and siRNA sequence protection in cell culture.

  1. GPU Accelerated Vector Median Filter

    NASA Technical Reports Server (NTRS)

    Aras, Rifat; Shen, Yuzhong

    2011-01-01

    Noise reduction is an important step for most image processing tasks. For three channel color images, a widely used technique is vector median filter in which color values of pixels are treated as 3-component vectors. Vector median filters are computationally expensive; for a window size of n x n, each of the n(sup 2) vectors has to be compared with other n(sup 2) - 1 vectors in distances. General purpose computation on graphics processing units (GPUs) is the paradigm of utilizing high-performance many-core GPU architectures for computation tasks that are normally handled by CPUs. In this work. NVIDIA's Compute Unified Device Architecture (CUDA) paradigm is used to accelerate vector median filtering. which has to the best of our knowledge never been done before. The performance of GPU accelerated vector median filter is compared to that of the CPU and MPI-based versions for different image and window sizes, Initial findings of the study showed 100x improvement of performance of vector median filter implementation on GPUs over CPU implementations and further speed-up is expected after more extensive optimizations of the GPU algorithm .

  2. Automated image segmentation using support vector machines

    NASA Astrophysics Data System (ADS)

    Powell, Stephanie; Magnotta, Vincent A.; Andreasen, Nancy C.

    2007-03-01

    Neurodegenerative and neurodevelopmental diseases demonstrate problems associated with brain maturation and aging. Automated methods to delineate brain structures of interest are required to analyze large amounts of imaging data like that being collected in several on going multi-center studies. We have previously reported on using artificial neural networks (ANN) to define subcortical brain structures including the thalamus (0.88), caudate (0.85) and the putamen (0.81). In this work, apriori probability information was generated using Thirion's demons registration algorithm. The input vector consisted of apriori probability, spherical coordinates, and an iris of surrounding signal intensity values. We have applied the support vector machine (SVM) machine learning algorithm to automatically segment subcortical and cerebellar regions using the same input vector information. SVM architecture was derived from the ANN framework. Training was completed using a radial-basis function kernel with gamma equal to 5.5. Training was performed using 15,000 vectors collected from 15 training images in approximately 10 minutes. The resulting support vectors were applied to delineate 10 images not part of the training set. Relative overlap calculated for the subcortical structures was 0.87 for the thalamus, 0.84 for the caudate, 0.84 for the putamen, and 0.72 for the hippocampus. Relative overlap for the cerebellar lobes ranged from 0.76 to 0.86. The reliability of the SVM based algorithm was similar to the inter-rater reliability between manual raters and can be achieved without rater intervention.

  3. Data Analysis for the SOLIS Vector Spectromagnetograph

    NASA Technical Reports Server (NTRS)

    Jones, Harrison P.; Harvey, John W.; Oegerle, William (Technical Monitor)

    2002-01-01

    The National Solar Observatory's SOLIS Vector Spectromagnetograph (VSM), which will produce three or more full-disk maps of the Sun's photospheric vector magnetic field every day for at least one solar magnetic cycle, is in the final stages of assembly. Initial observations, including cross-calibration with the current NASA/NSO spectromagnetograph (SPM) will soon be carried out at a test site in Tucson. This paper discusses data analysis techniques for reducing the raw data, calculation of line-of-sight magnetograms and both quick-look and high-precision inference of vector fields from Stokes spectral profiles. Existing SPM algorithms, suitably modified to accomodate the cameras, scanning pattern, and polarization calibration optics for the VSM, will be used to "clean" the raw data and to process line-of-sight, magnetograms. A recent. version of the High Altitude Observatory Milne-Eddington (HAO-ME) inversion code (Skumanich and Lites; 1987, 11)J 322, p. 473) will he used for high-precision vector fields since the algorithm has been extensively tested, is well understood, and is fast enough to complete data analysis within 24 hours of data acquisition. The simplified inversion algorithm of Auer, Heasley. arid House (1977, Sol. Phys. 55, p. 47) forms the initial guess for this version of the HAO-ME code and will be used for quick-look vector analysis of VSM data since its performance on simulated Stokes profiles is better than other candidate methods. Improvements (e.g., principal components analysis or neural networks) are under consideration and will be straightforward to implement. However, current resources are sufficient to store the original Stokes profiles only long enough for high-precision analysis. Retrospective reduction of Stokes data with improved methods will not be possible, and modifications will only be introduced when the advantages of doing so are compelling enough to justify discontinuity in the long-term data stream.

  4. A Dynamic Network Model to Explain the Development of Excellent Human Performance

    PubMed Central

    Den Hartigh, Ruud J. R.; Van Dijk, Marijn W. G.; Steenbeek, Henderien W.; Van Geert, Paul L. C.

    2016-01-01

    Across different domains, from sports to science, some individuals accomplish excellent levels of performance. For over 150 years, researchers have debated the roles of specific nature and nurture components to develop excellence. In this article, we argue that the key to excellence does not reside in specific underlying components, but rather in the ongoing interactions among the components. We propose that excellence emerges out of dynamic networks consisting of idiosyncratic mixtures of interacting components such as genetic endowment, motivation, practice, and coaching. Using computer simulations we demonstrate that the dynamic network model accurately predicts typical properties of excellence reported in the literature, such as the idiosyncratic developmental trajectories leading to excellence and the highly skewed distributions of productivity present in virtually any achievement domain. Based on this novel theoretical perspective on excellent human performance, this article concludes by suggesting policy implications and directions for future research. PMID:27148140

  5. Team performance and collective efficacy in the dynamic psychology of competitive team: a Bayesian network analysis.

    PubMed

    Fuster-Parra, P; García-Mas, A; Ponseti, F J; Leo, F M

    2015-04-01

    The purpose of this paper was to discover the relationships among 22 relevant psychological features in semi-professional football players in order to study team's performance and collective efficacy via a Bayesian network (BN). The paper includes optimization of team's performance and collective efficacy using intercausal reasoning pattern which constitutes a very common pattern in human reasoning. The BN is used to make inferences regarding our problem, and therefore we obtain some conclusions; among them: maximizing the team's performance causes a decrease in collective efficacy and when team's performance achieves the minimum value it causes an increase in moderate/high values of collective efficacy. Similarly, we may reason optimizing team collective efficacy instead. It also allows us to determine the features that have the strongest influence on performance and which on collective efficacy. From the BN two different coaching styles were differentiated taking into account the local Markov property: training leadership and autocratic leadership.

  6. Performance of solar collector arrays and collector controllers in the National Solar Data Network

    NASA Astrophysics Data System (ADS)

    Logee, T. L.; Kendall, P. W.

    1984-07-01

    The accumulated National Solar Data Network (NSDN) data has been analyzed with regard to collector and collector control performance. The collector data is presented in the ASHRAE format as efficiency vs. operating points, (Tinlet - Tambient)/insolation. Collector controls were analyzed by determining the losses caused by control problems common to the NSDN solar systems. This study of collectors and collector controls has several objectives which are: (1) to compare actual and predicted collector performance; (2) to determine which generic types of components performed well and which performed poorly; (3) to determine why predicted performance was not achieved in the field; (4) to determine the types and causes of failures; (5) to determine the reliability weaknesses; and (6) to determine whether there are any component integration problems.

  7. Theoretical Investigation of Optical WDM Network Performance in the Presence of FWM and ASE Noise

    NASA Astrophysics Data System (ADS)

    Iyer, Sridhar; Joy, Ambily

    2017-03-01

    In this article, for an optical star wavelength division multiplexing (WDM) network, with quality factor (Q-factor) as performance metric, we investigate the performance degradation due to the combined effects of four-wave mixing (FWM) and amplified spontaneous emission (ASE) noise. A mathematical model is developed, and the simulations are performed based on the optical frequency grid defined by the ITU-T Recommendation G.692. Further, the analysis is conducted for the optical fibers that are ITU-T compliant viz. G.652, G. 652D, G. 653, G. 654 and G.655. The simulation results show that, compared to the other fiber types, performance of the G. 652D and G.652 fibers is the "best", thus justifying the preferred use of fibers with high dispersion and effective area values. The simulation results also highlight that with the use of a fiber having low dispersion and effective area value, it may not be possible to obtain the desired performance.

  8. Predicting subcontractor performance using web-based Evolutionary Fuzzy Neural Networks.

    PubMed

    Ko, Chien-Ho

    2013-01-01

    Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi-cut-point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism.

  9. Performance of the GLOBALink/HF Network during the Halloween Storm Period of 2003

    NASA Astrophysics Data System (ADS)

    Goodman, J. M.; Patterson, J. D.

    2004-12-01

    The GLOBALink/HF system, developed and managed by ARINC, is a global high frequency data link communications network providing service to commercial aviation worldwide. It consists of 14 ground stations located around the globe, and a network control center located in Annapolis. The system was designed to provide reliable aircraft communications through the use of multi-station accessibility, quasi-dynamic frequency management, and a robust time-diversity modem with equalization. Although HF (i.e., 3-30 MHz) signaling has a poor reputation when considering individual circuits, it has been shown that near-real time channel evaluation and/or adaptive frequency management can improve performance considerably. Moreover, multi-station network operation provides an additional form of diversity, which is probably the most valuable design strategy. Our paper briefly describes the system, but the major discussion will be about performance metrics derived during super storms. The Halloween storm period of October-November 2003 was a period of significant ionospheric effects. Large geomagnetic storms were evidenced. We have examined the impact on HFDL of the various phenomena observed during this period. We have found some impact on HFDL performance for the October 29-31 period, but it is minimal in amplitude. While HFDL is based upon HF propagation, a medium known for its vulnerability to ionospheric variability, the system performance metric does not reflect this vulnerability to a significant degree. This is thought to be the result of the substantial amount of diversity built into the system, especially the adaptive frequency management system, Dynacastr, a system developed by RPSI. The adaptive frequency management system involves the use of active frequency tables (or AFTs) that are based upon space weather observables. During the stormy weeks of October and November, ARINC issued over seven changes to the AFTs used by every HFDL station. These changes helped the HFDL

  10. Link Performance Analysis for a Proposed Future Architecture of the Air Force Satellite Control Network

    DTIC Science & Technology

    2011-12-01

    Force Institute of Technology Air University Air Education and Training Command In Partial Fulfillment of the Requirements for the Degree of...computing is an interesting approach to link performance prediction. A paper was authored by the Global Educational Network for Spacecraft Operations...GENSO is a conglomerate of multiple ground stations shared by educational organizations most of which need access to LEO spacecraft. As with any

  11. Preparation of Tunable 3D Pillared Carbon Nanotube-Graphene Networks for High-Performance Capacitance

    DTIC Science & Technology

    2011-01-01

    puter modeling has predicted that such a 3D pillared VACNT graphene structure can be used for efficient hydrogen storage after being doped with...Pillared Carbon Nanotube Graphene Networks for High-Performance Capacitance Feng Du,†,§ Dingshan Yu,†,§ Liming Dai,†,* S. Ganguli,‡ V. Varshney,‡ and A...nanotubes (CNTs) and two-dimensional (2D) single-atomic layer graphene , have been demonstrated to show superior thermal, electrical, and mechanical

  12. Performance Analysis of AODV Routing Protocol for Wireless Sensor Network based Smart Metering

    NASA Astrophysics Data System (ADS)

    >Hasan Farooq, Low Tang Jung,

    2013-06-01

    Today no one can deny the need for Smart Grid and it is being considered as of utmost importance to upgrade outdated electric infrastructure to cope with the ever increasing electric load demand. Wireless Sensor Network (WSN) is considered a promising candidate for internetworking of smart meters with the gateway using mesh topology. This paper investigates the performance of AODV routing protocol for WSN based smart metering deployment. Three case studies are presented to analyze its performance based on four metrics of (i) Packet Delivery Ratio, (ii) Average Energy Consumption of Nodes (iii) Average End-End Delay and (iv) Normalized Routing Load.

  13. Analysing the Correlation between Social Network Analysis Measures and Performance of Students in Social Network-Based Engineering Education

    ERIC Educational Resources Information Center

    Putnik, Goran; Costa, Eric; Alves, Cátia; Castro, Hélio; Varela, Leonilde; Shah, Vaibhav

    2016-01-01

    Social network-based engineering education (SNEE) is designed and implemented as a model of Education 3.0 paradigm. SNEE represents a new learning methodology, which is based on the concept of social networks and represents an extended model of project-led education. The concept of social networks was applied in the real-life experiment,…

  14. Using HIPPI switches to build high-performance multiple FDDI ring networks

    NASA Astrophysics Data System (ADS)

    Gilbert, Thomas A.

    1992-03-01

    It is a commonplace observation that computational power at the desktop is increasing at an exponential rate. This continues two decades after the first single chip VLSI microprocessor became commercially available and it is projected to continue for at least another decade. As a direct consequence, several observations can be made about the revolutionary impacts occurring in data networking: (1) Inexpensive computer power has made it economically feasible to distribute immense computational capacity to the desktop. (2) Distribution has created a demand for sophisticated networks to enable resource sharing among work groups. (3) Placing compute capacity at the point of consumption has removed the communication barrier from the `man/machine' interface. Virtually every user of computer systems is presented with increasingly rich visual paradigms. Current graphical user interfaces are designed to take advantage of bit mapped color displays that have spatial resolutions of 1024 pixels X 1280 pixels and 8 to 24 bits per pixel of color resolution. (4) Standards have been defined and systems are being built to extend the visual paradigm over the networks that interconnect information workers. (5) As a result of the exponential increase in computing capacity available for constant dollars, one would expect the demand networking capacity to increase accordingly. However, as a consequence of observation (4), the rate of increase is far greater. One of the narrow effects of the above has been to accelerate the demand for high performance networking solutions to support the burgeoning users of PCs and workstations. Fiber distributed data interface (FDDI) standard based bridges and routers have received rapid acceptance to provide backbone connections among Ethernet segments. It is not uncommon for an organization to have dozens of Ethernets within a single establishment. The cost of FDDI compatible interface boards for workstations and PCs is declining rapidly. This year the

  15. 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.

  16. Performance issues in SCM label switched networks due to tunable laser switching events

    NASA Astrophysics Data System (ADS)

    Smyth, F.; Barry, L. P.

    2006-09-01

    Optical Packet Switched (OPS) networks employing Optical Label Switching (OLS) techniques have the potential to enable an all-optical internet. In these networks, data remains in optical format throughout the entire network and routing is performed using a separate optical label. The label information is used to control fast tunable lasers that will transfer data packets to different wavelengths for routing and contention resolution. In this paper we investigate interference between subcarrier multiplexed (SCM) labels in such a network, due to switching events in the tunable laser transmitter. This interference may place a limitation on the channel spacing and subcarrier frequency used. Two 50GHz spaced optical carriers were modulated with 2.5Gbit/s SCM labels at 20GHz. Bit error rate measurements were taken with two lasers fixed 50 GHz apart, and also with one of the lasers (an SG-DBR) switching between this channel and another one 800GHz away. When the SG-DBR laser is not switching, a power penalty of approximately 0.25 dB is introduced due to interference through the optical filter. However, when the SG-DBR laser is switching between wavelengths an error floor of 1x10-5 is introduced due to the time it takes the tunable laser to settle to its target channel. In a systems application, this would result in packets being incorrectly routed.

  17. Performance evaluation of neural network and linear predictors for near-lossless compression of EEG signals.

    PubMed

    Sriraam, N; Eswaran, C

    2008-01-01

    This paper presents a comparison of the performances of neural network and linear predictors for near-lossless compression of EEG signals. Three neural network predictors, namely, single-layer perceptron (SLP), multilayer perceptron (MLP), and Elman network (EN), and two linear predictors, namely, autoregressive model (AR) and finite-impulse response filter (FIR) are used. For all the predictors, uniform quantization is applied on the residue signals obtained as the difference between the original and the predicted values. The maximum allowable reconstruction error delta is varied to determine the theoretical bound delta 0 for near-lossless compression and the corresponding bit rate rp. It is shown that among all the predictors, the SLP yields the best results in achieving the lowest values for delta 0 and rp. The corresponding values of the fidelity parameters, namely, percent of root-mean-square difference, peak SNR and cross correlation are also determined. A compression efficiency of 82.8% is achieved using the SLP with a near-lossless bound delta 0 = 3, with the diagnostic quality of the reconstructed EEG signal preserved. Thus, the proposed near-lossless scheme facilitates transmission of real time as well as offline EEG signals over network to remote interpretation center economically with less bandwidth utilization compared to other known lossless and near-lossless schemes.

  18. A smartphone-based platform to test the performance of wireless mobile networks and preliminary findings

    NASA Astrophysics Data System (ADS)

    Geng, Xinli; Xu, Hao; Qin, Xiaowei

    2016-10-01

    During the last several years, the amount of wireless network traffic data increased fast and relative technologies evolved rapidly. In order to improve the performance and Quality of Experience (QoE) of wireless network services, the analysis of field network data and existing delivery mechanisms comes to be a promising research topic. In order to achieve this goal, a smartphone based platform named Monitor and Diagnosis of Mobile Applications (MDMA) was developed to collect field data. Based on this tool, the web browsing service of High Speed Downlink Packet Access (HSDPA) network was tested. The top 200 popular websites in China were selected and loaded on smartphone for thousands times automatically. Communication packets between the smartphone and the cell station were captured for various scenarios (e.g. residential area, urban roads, bus station etc.) in the selected city. A cross-layer database was constructed to support the off-line analysis. Based on the results of client-side experiments and analysis, the usability of proposed portable tool was verified. The preliminary findings and results for existing web browsing service were also presented.

  19. Three-dimensional interconnected nickel phosphide networks with hollow microstructures and desulfurization performance

    SciTech Connect

    Zhang, Shuna; Zhang, Shujuan; Song, Limin; Wu, Xiaoqing; Fang, Sheng

    2014-05-01

    Graphical abstract: Three-dimensional interconnected nickel phosphide networks with hollow microstructures and desulfurization performance. - Highlights: • Three-dimensional Ni{sub 2}P has been prepared using foam nickel as a template. • The microstructures interconnected and formed sponge-like porous networks. • Three-dimensional Ni{sub 2}P shows superior hydrodesulfurization activity. - Abstract: Three-dimensional microstructured nickel phosphide (Ni{sub 2}P) was fabricated by the reaction between foam nickel (Ni) and phosphorus red. The as-prepared Ni{sub 2}P samples, as interconnected networks, maintained the original mesh structure of foamed nickel. The crystal structure and morphology of the as-synthesized Ni{sub 2}P were characterized by X-ray diffraction, scanning electron microscopy, automatic mercury porosimetry and X-ray photoelectron spectroscopy. The SEM study showed adjacent hollow branches were mutually interconnected to form sponge-like networks. The investigation on pore structure provided detailed information for the hollow microstructures. The growth mechanism for the three-dimensionally structured Ni{sub 2}P was postulated and discussed in detail. To investigate its catalytic properties, SiO{sub 2} supported three-dimensional Ni{sub 2}P was prepared successfully and evaluated for the hydrodesulfurization (HDS) of dibenzothiophene (DBT). DBT molecules were mostly hydrogenated and then desulfurized by Ni{sub 2}P/SiO{sub 2}.

  20. Quantifying individual performance in Cricket — A network analysis of batsmen and bowlers

    NASA Astrophysics Data System (ADS)

    Mukherjee, Satyam

    2014-01-01

    Quantifying individual performance in the game of Cricket is critical for team selection in International matches. The number of runs scored by batsmen and wickets taken by bowlers serves as a natural way of quantifying the performance of a cricketer. Traditionally the batsmen and bowlers are rated on their batting or bowling average respectively. However, in a game like Cricket it is always important the manner in which one scores the runs or claims a wicket. Scoring runs against a strong bowling line-up or delivering a brilliant performance against a team with a strong batting line-up deserves more credit. A player’s average is not able to capture this aspect of the game. In this paper we present a refined method to quantify the ‘quality’ of runs scored by a batsman or wickets taken by a bowler. We explore the application of Social Network Analysis (SNA) to rate the players in a team performance. We generate a directed and weighted network of batsmen-bowlers using the player-vs-player information available for Test cricket and ODI cricket. Additionally we generate a network of batsmen and bowlers based on the dismissal record of batsmen in the history of cricket-Test (1877-2011) and ODI (1971-2011). Our results show that M. Muralitharan is the most successful bowler in the history of Cricket. Our approach could potentially be applied in domestic matches to judge a player’s performance which in turn paves the way for a balanced team selection for International matches.

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

    PubMed

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

    2017-02-16

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

  2. Evaluation of replacement protocols and modifications to TCP to enhance ASC Wide Area Network performance.

    SciTech Connect

    Romero, Randy L. Jr.

    2004-09-01

    Historically, TCP/IP has been the protocol suite used to transfer data throughout the Advanced Simulation and Computing (ASC) community. However, TCP was developed many years ago for an environment very different from the ASC Wide Area Network (WAN) of today. There have been numerous publications that hint of better performance if modifications were made to the TCP algorithms or a different protocol was used to transfer data across a high bandwidth, high delay WAN. Since Sandia National Laboratories wants to maximize the ASC WAN performance to support the Thor's Hammer supercomputer, there is strong interest in evaluating modifications to the TCP protocol and in evaluating alternatives to TCP, such as SCTP, to determine if they provide improved performance. Therefore, the goal of this project is to test, evaluate, compare, and report protocol technologies that enhance the performance of the ASC WAN.

  3. REMOTE, a Wireless Sensor Network Based System to Monitor Rowing Performance

    PubMed Central

    Llosa, Jordi; Vilajosana, Ignasi; Vilajosana, Xavier; Navarro, Nacho; Suriñach, Emma; Marquès, Joan Manuel

    2009-01-01

    In this paper, we take a hard look at the performance of REMOTE, a sensor network based application that provides a detailed picture of a boat movement, individual rower performance, or his/her performance compared with other crew members. The application analyzes data gathered with a WSN strategically deployed over a boat to obtain information on the boat and oar movements. Functionalities of REMOTE are compared to those of RowX [1] outdoor instrument, a commercial wired sensor instrument designed for similar purposes. This study demonstrates that with smart geometrical configuration of the sensors, rotation and translation of the oars and boat can be obtained. Three different tests are performed: laboratory calibration allows us to become familiar with the accelerometer readings and validate the theory, ergometer tests which help us to set the acquisition parameters, and on boat tests shows the application potential of this technologies in sports. PMID:22423204

  4. Performance analysis of Wald-statistic based network detection methods for radiation sources

    SciTech Connect

    Sen, Satyabrata; Rao, Nageswara S; Wu, Qishi; Barry, M. L..; Grieme, M.; Brooks, Richard R; Cordone, G.

    2016-01-01

    There have been increasingly large deployments of radiation detection networks that require computationally fast algorithms to produce prompt results over ad-hoc sub-networks of mobile devices, such as smart-phones. These algorithms are in sharp contrast to complex network algorithms that necessitate all measurements to be sent to powerful central servers. In this work, at individual sensors, we employ Wald-statistic based detection algorithms which are computationally very fast, and are implemented as one of three Z-tests and four chi-square tests. At fusion center, we apply the K-out-of-N fusion to combine the sensors hard decisions. We characterize the performance of detection methods by deriving analytical expressions for the distributions of underlying test statistics, and by analyzing the fusion performances in terms of K, N, and the false-alarm rates of individual detectors. We experimentally validate our methods using measurements from indoor and outdoor characterization tests of the Intelligence Radiation Sensors Systems (IRSS) program. In particular, utilizing the outdoor measurements, we construct two important real-life scenarios, boundary surveillance and portal monitoring, and present the results of our algorithms.

  5. DeGNServer: deciphering genome-scale gene networks through high performance reverse engineering analysis.

    PubMed

    Li, Jun; Wei, Hairong; Zhao, Patrick Xuechun

    2013-01-01

    Analysis of genome-scale gene networks (GNs) using large-scale gene expression data provides unprecedented opportunities to uncover gene interactions and regulatory networks involved in various biological processes and developmental programs, leading to accelerated discovery of novel knowledge of various biological processes, pathways and systems. The widely used context likelihood of relatedness (CLR) method based on the mutual information (MI) for scoring the similarity of gene pairs is one of the accurate methods currently available for inferring GNs. However, the MI-based reverse engineering method can achieve satisfactory performance only when sample size exceeds one hundred. This in turn limits their applications for GN construction from expression data set with small sample size. We developed a high performance web server, DeGNServer, to reverse engineering and decipher genome-scale networks. It extended the CLR method by integration of different correlation methods that are suitable for analyzing data sets ranging from moderate to large scale such as expression profiles with tens to hundreds of microarray hybridizations, and implemented all analysis algorithms using parallel computing techniques to infer gene-gene association at extraordinary speed. In addition, we integrated the SNBuilder and GeNa algorithms for subnetwork extraction and functional module discovery. DeGNServer is publicly and freely available online.

  6. Interconnected Nanoflake Network Derived from a Natural Resource for High-Performance Lithium-Ion Batteries.

    PubMed

    Cheng, Fei; Li, Wen-Cui; Lu, An-Hui

    2016-10-06

    Numerous natural resources have a highly interconnected network with developed porous structure, so enabling directional and fast matrix transport. Such structures are appealing for the design of efficient anode materials for lithium-ion batteries, although they can be challenging to prepare. Inspired by nature, a novel synthesis route from biomass is proposed by using readily available auricularia as retractable support and carbon coating precursor to soak up metal salt solution. Using the swelling properties of the auricularia with the complexation of metal ions, a nitrogen-containing MnO@C nanoflake network has been easily synthesized with fast electrochemical reaction dynamics and a superior lithium storage performance. A subsequent carbonization results in the in situ synthesis of MnO nanoparticles throughout the porous carbon flake network. When evaluated as an anode material for lithium-ion batteries, an excellent reversible capacity is achieved of 868 mA h g(-1) at 0.2 A g(-1) over 300 cycles and 668 mA h g(-1) at 1 A g(-1) over 500 cycles, indicating a high tolerance to the volume expansion. The approach investigated opens up new avenues for the design of high performance electrodes with highly cross-linked nanoflake structures, which may have great application prospects.

  7. EFFECT OF MOBILITY ON PERFORMANCE OF WIRELESS AD-HOC NETWORK PROTOCOLS.

    SciTech Connect

    Barrett, C. L.; Drozda, M.; Marathe, M. V.; Marathe, A.

    2001-01-01

    We empirically study the effect of mobility on the performance of protocols designed for wireless adhoc networks. An important ohjective is to study the interaction of the Routing and MAC layer protocols under different mobility parameters. We use three basic mobility models: grid mobility model, random waypoint model, and exponential correlated random model. The performance of protocols was measured in terms of (i) latency, (ii) throughput, (iii) number of packels received, (iv) long term fairness and (v) number of control packets at the MAC layer level. Three different commonly studied routing protocols were used: AODV, DSR and LAR1. Similarly three well known MAC protocols were used: MACA, 802.1 1 and CSMA. The inair1 conclusion of our study include the following: 1. 'I'he performance of the: network varies widely with varying mobility models, packet injection rates and speeds; and can ba in fact characterized as fair to poor depending on the specific situation. Nevertheless, in general, it appears that the combination of AODV and 802.1 I is far better than other combination of routing and MAC protocols. 2. MAC layer protocols interact with routing layer protocols. This concept which is formalized using statistics implies that in general it is not meaningful to speak about a MAC or a routing protocol in isolation. Such an interaction leads to trade-offs between the amount of control packets generated by each layer. More interestingly, the results wise the possibility of improving the performance of a particular MAC layer protocol by using a cleverly designed routing protocol or vice-versa. 3. Routing prolocols with distributed knowledge about routes are more suitable for networks with mobility. This is seen by comparing the performance of AODV with DSR or LAR scheme 1. In DSli and IAR scheme 1, information about a computed path is being stored in the route query control packct. 4. MAC layer protocols have varying performance with varying mobility models. It is

  8. Distinguishing terminal monophyletic groups from reticulate taxa: performance of phenetic, tree-based, and network procedures.

    PubMed

    Reeves, Patrick A; Richards, Christopher M

    2007-04-01

    Hybridization is a well-documented, natural phenomenon that is common at low taxonomic levels in the higher plants and other groups. In spite of the obvious potential for gene flow via hybridization to cause reticulation in an evolutionary tree, analytical methods based on a strictly bifurcating model of evolution have frequently been applied to data sets containing taxa known to hybridize in nature. Using simulated data, we evaluated the relative performance of phenetic, tree-based, and network approaches for distinguishing between taxa with known reticulate history and taxa that were true terminal monophyletic groups. In all methods examined, type I error (the erroneous rejection of the null hypothesis that a taxon of interest is not monophyletic) was likely during the early stages of introgressive hybridization. We used the gradual erosion of type I error with continued gene flow as a metric for assessing relative performance. Bifurcating tree-based methods performed poorly, with highly supported, incorrect topologies appearing during some phases of the simulation. Based on our model, we estimate that many thousands of gene flow events may be required in natural systems before reticulate taxa will be reliably detected using tree-based methods of phylogeny reconstruction. We conclude that the use of standard bifurcating tree-based methods to identify terminal monophyletic groups for the purposes of defining or delimiting phylogenetic species, or for prioritizing populations for conservation purposes, is difficult to justify when gene flow between sampled taxa is possible. As an alternative, we explored the use of two network methods. Minimum spanning networks performed worse than most tree-based methods and did not yield topologies that were easily interpretable as phylogenies. The performance of NeighborNet was comparable to parsimony bootstrap analysis. NeighborNet and reverse successive weighting were capable of identifying an ephemeral signature of reticulate

  9. Comparison of the Performances of Five Primer Sets for the Detection and Quantification of Plasmodium in Anopheline Vectors by Real-Time PCR

    PubMed Central

    Chaumeau, V.; Andolina, C.; Fustec, B.; Tuikue Ndam, N.; Brengues, C.; Herder, S.; Cerqueira, D.; Chareonviriyaphap, T.; Nosten, F.; Corbel, V.

    2016-01-01

    Quantitative real-time polymerase chain reaction (qrtPCR) has made a significant improvement for the detection of Plasmodium in anopheline vectors. A wide variety of primers has been used in different assays, mostly adapted from molecular diagnosis of malaria in human. However, such an adaptation can impact the sensitivity of the PCR. Therefore we compared the sensitivity of five primer sets with different molecular targets on blood stages, sporozoites and oocysts standards of Plasmodium falciparum (Pf) and P. vivax (Pv). Dilution series of standard DNA were used to discriminate between methods at low concentrations of parasite and to generate standard curves suitable for the absolute quantification of Plasmodium sporozoites. Our results showed that the best primers to detect blood stages were not necessarily the best ones to detect sporozoites. Absolute detection threshold of our qrtPCR assay varied between 3.6 and 360 Pv sporozoites and between 6 and 600 Pf sporozoites per mosquito according to the primer set used in the reaction mix. In this paper, we discuss the general performance of each primer set and highlight the need to use efficient detection methods for transmission studies. PMID:27441839

  10. Comparison of the Performances of Five Primer Sets for the Detection and Quantification of Plasmodium in Anopheline Vectors by Real-Time PCR.

    PubMed

    Chaumeau, V; Andolina, C; Fustec, B; Tuikue Ndam, N; Brengues, C; Herder, S; Cerqueira, D; Chareonviriyaphap, T; Nosten, F; Corbel, V

    2016-01-01

    Quantitative real-time polymerase chain reaction (qrtPCR) has made a significant improvement for the detection of Plasmodium in anopheline vectors. A wide variety of primers has been used in different assays, mostly adapted from molecular diagnosis of malaria in human. However, such an adaptation can impact the sensitivity of the PCR. Therefore we compared the sensitivity of five primer sets with different molecular targets on blood stages, sporozoites and oocysts standards of Plasmodium falciparum (Pf) and P. vivax (Pv). Dilution series of standard DNA were used to discriminate between methods at low concentrations of parasite and to generate standard curves suitable for the absolute quantification of Plasmodium sporozoites. Our results showed that the best primers to detect blood stages were not necessarily the best ones to detect sporozoites. Absolute detection threshold of our qrtPCR assay varied between 3.6 and 360 Pv sporozoites and between 6 and 600 Pf sporozoites per mosquito according to the primer set used in the reaction mix. In this paper, we discuss the general performance of each primer set and highlight the need to use efficient detection methods for transmission studies.

  11. Production of lentiviral vectors

    PubMed Central

    Merten, Otto-Wilhelm; Hebben, Matthias; Bovolenta, Chiara

    2016-01-01

    Lentiviral vectors (LV) have seen considerably increase in use as gene therapy vectors for the treatment of acquired and inherited diseases. This review presents the state of the art of the production of these vectors with particular emphasis on their large-scale production for clinical purposes. In contrast to oncoretroviral vectors, which are produced using stable producer cell lines, clinical-grade LV are in most of the cases produced by transient transfection of 293 or 293T cells grown in cell factories. However, more recent developments, also, tend to use hollow fiber reactor, suspension culture processes, and the implementation of stable producer cell lines. As is customary for the biotech industry, rather sophisticated downstream processing protocols have been established to remove any undesirable process-derived contaminant, such as plasmid or host cell DNA or host cell proteins. This review compares published large-scale production and purification processes of LV and presents their process performances. Furthermore, developments in the domain of stable cell lines and their way to the use of production vehicles of clinical material will be presented. PMID:27110581

  12. Optimal routing in general finite multi-server queueing networks.

    PubMed

    van Woensel, Tom; Cruz, Frederico R B

    2014-01-01

    The design of general finite multi-server queueing networks is a challenging problem that arises in many real-life situations, including computer networks, manufacturing systems, and telecommunication networks. In this paper, we examine the optimal routing problem in arbitrary configured acyclic queueing networks. The performance of the finite queueing network is evaluated with a known approximate performance evaluation method and the optimization is done by means of a heuristics based on the Powell algorithm. The proposed methodology is then applied to determine the optimal routing probability vector that maximizes the throughput of the queueing network. We show numerical results for some networks to quantify the quality of the routing vector approximations obtained.

  13. Improving TCP Network Performance by Detecting and Reacting to Packet Reordering

    NASA Technical Reports Server (NTRS)

    Kruse, Hans; Ostermann, Shawn; Allman, Mark

    2003-01-01

    There are many factors governing the performance of TCP-basec applications traversing satellite channels. The end-to-end performance of TCP is known to be degraded by the reordering, delay, noise and asymmetry inherent in geosynchronous systems. This result has been largely based on experiments that evaluate the performance of TCP in single flow tests. While single flow tests are useful for deriving information on the theoretical behavior of TCP and allow for easy diagnosis of problems they do not represent a broad range of realistic situations and therefore cannot be used to authoritatively comment on performance issues. The experiments discussed in this report test TCP s performance in a more dynamic environment with competing traffic flows from hundreds of TCP connections running simultaneously across the satellite channel. Another aspect we investigate is TCP's reaction to bit errors on satellite channels. TCP interprets loss as a sign of network congestion. This causes TCP to reduce its transmission rate leading to reduced performance when loss is due to corruption. We allowed the bit error rate on our satellite channel to vary widely and tested the performance of TCP as a function of these bit error rates. Our results show that the average performance of TCP on satellite channels is good even under conditions of loss as high as bit error rates of 10(exp -5)

  14. A model to compare performance of space and ground network support of low-Earth orbiters

    NASA Technical Reports Server (NTRS)

    Posner, E. C.

    1992-01-01

    This article compares the downlink performance in a gross average sense between space and ground network support of low-Earth orbiters. The purpose is to assess what the demand for DSN support of future small, low-cost missions might be, if data storage for spacecraft becomes reliable enough and small enough to support the storage requirements needed to enable support only a fraction of the time. It is shown that the link advantage of the DSN over space reception in an average sense is enormous for low-Earth orbiters. The much shorter distances needed to communicate with the ground network more than make up for the speedup in data rate needed to compensate for the short contact times with the DSN that low-Earth orbiters have. The result is that more and more requests for DSN-only support of low-Earth orbiters can be expected.

  15. Modeling network infrastructure and performance evaluation for PACS: DICOM over ethernet-based TCP/IP

    NASA Astrophysics Data System (ADS)

    Arvanitis, Theodoros N.; Roth, David

    2001-08-01

    Studies of information and process modeling have demonstrated the importance and clinical impact of Picture Archiving and Communication Systems in the efficient operational management of imaging within the clinical setting. The appropriate identification of both clinical and technology requirements for the planning and deployment of such systems is essential to achieve cost-effectiveness in use. The understanding of the complexities of clinically viable network topologies and architectures for PACS can be achieved through realistic simulations and modeling. The purpose of this paper is to provide a methodology for modeling the DICOM session and application layer entities over Ethernet-based TCP/IP, by using the OPNET Modeler, and derive performance evaluation metrics for different PACS network topologies.

  16. Structural influence of the inorganic network in the laser performance of dye-doped hybrid materials

    NASA Astrophysics Data System (ADS)

    Costela, A.; García-Moreno, I.; García, O.; del Agua, D.; Sastre, R.

    2005-05-01

    We report a systematic study of the influence on the laser action of Rhodamine 6G (Rh6G) of the composition and structure of new hybrid matrices based on 2-hydroxyethyl methacrylate (HEMA) as organic monomer and different weight proportions of dimethyldiethoxysilane (DEOS) and tetraethoxysilane (TEOS) as inorganic part. We selected mixtures of di- and tetra-functionalized alkoxides trying to decrease, in a controlled way, the rigidity of the three-dimensional network by making use of the flexibility provided by the linear chains acting as a spacer of the inorganic domains. The organization of the molecular units in these nanomaterials was studied through a structural analysis by solid-state NMR. The different reactivity exhibited by di- and tetra-functionalized silanols generates a non-homogeneous tri-dimensional network. Thus, the laser performance in dye-doped hybrid materials is improved when the inorganic phase is composed of a unique alkoxide.

  17. Design and Performance of the Acts Gigabit Satellite Network High Data-Rate Ground Station

    NASA Technical Reports Server (NTRS)

    Hoder, Doug; Kearney, Brian

    1995-01-01

    The ACTS High Data-Rate Ground stations were built to support the ACTS Gigabit Satellite Network (GSN). The ACTS GSN was designed to provide fiber-compatible SONET service to remote nodes and networks through a wideband satellite system. The ACTS satellite is unique in its extremely wide bandwidth, and electronically controlled spot beam antennas. This paper discusses the requirements, design and performance of the RF section of the ACTS High Data-Rate Ground Stations and constituent hardware. The ACTS transponder systems incorporate highly nonlinear hard limiting. This introduced a major complexity in to the design and subsequent modification of the ground stations. A discussion of the peculiarities of the A CTS spacecraft transponder system and their impact is included.

  18. Improving performance of long-term care networks at their initial stage: an empirical study of factors affecting results.

    PubMed

    Angiola, Nunzio; Bianchi, Piervito

    2016-06-10

    Until now very little research has been carried out on the performance of health and human services networks in evolution. In particular, previous studies mainly referred to "centrally governed services networks" in the US context. According to Provan and Kenis (2008), these networks are "lead organization-governed", and are different from the "participant-governed" model or the "network administrative organization (NAO)" solution. We focused our attention on the Apulia region care services networks (Italy). In the last few years, the governance of these networks has passed from the "participant-governed" model to the NAO approach. We examined how the integration mechanisms work in this type of networks, and if there were challenges to tackle in order to improve their overall performance. These networks were examined at their initial stage, exactly when their governance model moved to a more integrated solution. We collected survey data from 17 health and human services networks out of 45 (38%). The research is carried out by means of statistical methods (OLS). The analysis is cross sectional. The implementation of "rational/technocratic" factors is important but not sufficient to enhance collaboration. The integration at the "professional level" should be kept in mind. In particular, the role of network (case) managers is paramount. Copyright © 2016 John Wiley & Sons, Ltd.

  19. Impact of pay-for-performance contracts and network registry on diabetes and asthma HEDIS measures in an integrated delivery network.

    PubMed

    Levin-Scherz, Jeffrey; DeVita, Nicole; Timbie, Justin

    2006-02-01

    This article reviews the experience of a large, heterogeneous integrated delivery network that incorporated physician quality metrics into pay-for-performance contracts. The authors present criteria for including measures in pay-for-performance contracts and offer a practical approach to determining withhold return or bonus distribution based on improvement and performance. They demonstrate interventions undertaken to improve performance, including the development of a claims-based registry. Empirical data show that the network performance improved more than the comparable state and national performance during the period of this observational study. The authors conclude that pay-for-performance contracts led to development of medical management programs including a claims-based registry and nonphysician interventions, which helped significantly improve selected HEDIS scores.

  20. Investigating the performance of neural network backpropagation algorithms for TEC estimations using South African GPS data

    NASA Astrophysics Data System (ADS)

    Habarulema, J. B.; McKinnell, L.-A.

    2012-05-01

    In this work, results obtained by investigating the application of different neural network backpropagation training algorithms are presented. This was done to assess the performance accuracy of each training algorithm in total electron content (TEC) estimations using identical datasets in models development and verification processes. Investigated training algorithms are standard backpropagation (SBP), backpropagation with weight delay (BPWD), backpropagation with momentum (BPM) term, backpropagation with chunkwise weight update (BPC) and backpropagation for batch (BPB) training. These five algorithms are inbuilt functions within the Stuttgart Neural Network Simulator (SNNS) and the main objective was to find out the training algorithm that generates the minimum error between the TEC derived from Global Positioning System (GPS) observations and the modelled TEC data. Another investigated algorithm is the MatLab based Levenberg-Marquardt backpropagation (L-MBP), which achieves convergence after the least number of iterations during training. In this paper, neural network (NN) models were developed using hourly TEC data (for 8 years: 2000-2007) derived from GPS observations over a receiver station located at Sutherland (SUTH) (32.38° S, 20.81° E), South Africa. Verification of the NN models for all algorithms considered was performed on both "seen" and "unseen" data. Hourly TEC values over SUTH for 2003 formed the "seen" dataset. The "unseen" dataset consisted of hourly TEC data for 2002 and 2008 over Cape Town (CPTN) (33.95° S, 18.47° E) and SUTH, respectively. The models' verification showed that all algorithms investigated provide comparable results statistically, but differ significantly in terms of time required to achieve convergence during input-output data training/learning. This paper therefore provides a guide to neural network users for choosing appropriate algorithms based on the availability of computation capabilities used for research.

  1. Control mechanism to prevent correlated message arrivals from degrading signaling no. 7 network performance

    NASA Astrophysics Data System (ADS)

    Kosal, Haluk; Skoog, Ronald A.

    1994-04-01

    Signaling System No. 7 (SS7) is designed to provide a connection-less transfer of signaling messages of reasonable length. Customers having access to user signaling bearer capabilities as specified in the ANSI T1.623 and CCITT Q.931 standards can send bursts of correlated messages (e.g., by doing a file transfer that results in the segmentation of a block of data into a number of consecutive signaling messages) through SS7 networks. These message bursts with short interarrival times could have an adverse impact on the delay performance of the SS7 networks. A control mechanism, Credit Manager, is investigated in this paper to regulate incoming traffic to the SS7 network by imposing appropriate time separation between messages when the incoming stream is too bursty. The credit manager has a credit bank where credits accrue at a fixed rate up to a prespecified credit bank capacity. When a message arrives, the number of octets in that message is compared to the number of credits in the bank. If the number of credits is greater than or equal to the number of octets, then the message is accepted for transmission and the number of credits in the bank is decremented by the number of octets. If the number of credits is less than the number of octets, then the message is delayed until enough credits are accumulated. This paper presents simulation results showing delay performance of the SS7 ISUP and TCAP message traffic with a range of correlated message traffic, and control parameters of the credit manager (i.e., credit generation rate and bank capacity) are determined that ensure the traffic entering the SS7 network is acceptable. The results show that control parameters can be set so that for any incoming traffic stream there is no detrimental impact on the SS7 ISUP and TCAP message delay, and the credit manager accepts a wide range of traffic patterns without causing significant delay.

  2. Networks.

    ERIC Educational Resources Information Center

    Maughan, George R.; Petitto, Karen R.; McLaughlin, Don

    2001-01-01

    Describes the connectivity features and options of modern campus communication and information system networks, including signal transmission (wire-based and wireless), signal switching, convergence of networks, and network assessment variables, to enable campus leaders to make sound future-oriented decisions. (EV)

  3. Seismic Network Performance Estimation: Comparing Predictions of Magnitude of Completeness and Location Accuracy to Observations from an Earthquake Catalogue

    NASA Astrophysics Data System (ADS)

    Spriggs, N.; Greig, D. W.; Ackerley, N. J.

    2014-12-01

    The design of seismic networks for the monitoring of induced seismicity is of critical importance. The recent introduction of regulations in various locations around the world (with more upcoming) has created a need for a priori confirmation that certain performance standards are met. We develop a tool to assess two key measures of network performance without an earthquake catalogue: magnitude of completeness and location accuracy. Site noise measurements are taken at existing seismic stations or as part of a noise survey. We then interpolate between measured values to determine a noise map for the entire region. The site noise is then summed with the instrument noise to determine the effective station noise at each of the proposed station locations. Location accuracy is evaluated by generating a covariance matrix that represents the error ellipsoid from the travel time derivatives (Peters and Crosson, 1972). To determine the magnitude of completeness we assume isotropic radiation and mandate a minimum signal to noise ratio for detection. For every gridpoint, we compute the Brune spectra for synthetic events and iterate to determine the smallest magnitude event that can be detected by at least four stations. We apply this methodology to an example network. We predict the magnitude of completeness and the location accuracy and compare the predicted values to observed values generated from the existing earthquake catalogue for the network. We discuss the effects of hypothetical station additions and removals on network performance to simulate network expansions and station failures. The ability to predict hypothetical station performance allows for the optimization of seismic network design and enables prediction of network performance even for a purely hypothetical seismic network. This allows the operators of networks for induced seismicity monitoring to be confident that performance criteria are met from day one of operations.

  4. 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…

  5. Novel L1 neural network adaptive control architecture with guaranteed transient performance.

    PubMed

    Cao, Chengyu; Hovakimyan, Naira

    2007-07-01

    In this paper, we present a novel neural network (NN) adaptive control architecture with guaranteed transient performance. With this new architecture, both input and output signals of an uncertain nonlinear system follow a desired linear system during the transient phase, in addition to stable tracking. This new architecture uses a low-pass filter in the feedback loop, which consequently enables to enforce the desired transient performance by increasing the adaptation gain. For the guaranteed transient performance of both input and output signals of the uncertain nonlinear system, the L1 gain of a cascaded system, comprised of the low-pass filter and the closed-loop desired reference model, is required to be less than the inverse of the Lipschitz constant of the unknown nonlinearities in the system. The tools from this paper can be used to develop a theoretically justified verification and validation framework for NN adaptive controllers. Simulation results illustrate the theoretical findings.

  6. Analytic network process model for sustainable lean and green manufacturing performance indicator

    NASA Astrophysics Data System (ADS)

    Aminuddin, Adam Shariff Adli; Nawawi, Mohd Kamal Mohd; Mohamed, Nik Mohd Zuki Nik

    2014-09-01

    Sustainable manufacturing is regarded as the most complex manufacturing paradigm to date as it holds the widest scope of requirements. In addition, its three major pillars of economic, environment and society though distinct, have some overlapping among each of its elements. Even though the concept of sustainability is not new, the development of the performance indicator still needs a lot of improvement due to its multifaceted nature, which requires integrated approach to solve the problem. This paper proposed the best combination of criteria en route a robust sustainable manufacturing performance indicator formation via Analytic Network Process (ANP). The integrated lean, green and sustainable ANP model can be used to comprehend the complex decision system of the sustainability assessment. The finding shows that green manufacturing is more sustainable than lean manufacturing. It also illustrates that procurement practice is the most important criteria in the sustainable manufacturing performance indicator.

  7. Improving Estimation Performance in Networked Control Systems Applying the Send-on-delta Transmission Method

    PubMed Central

    Nguyen, Vinh Hao; Suh, Young Soo

    2007-01-01

    This paper is concerned with improving performance of a state estimation problem over a network in which a send-on-delta (SOD) transmission method is used. The SOD method requires that a sensor node transmit data to the estimator node only if its measurement value changes more than a given specified δ value. This method has been explored and applied by researchers because of its efficiency in the network bandwidth improvement. However, when this method is used, it is not ensured that the estimator node receives data from the sensor nodes regularly at every estimation period. Therefore, we propose a method to reduce estimation error in case of no sensor data reception. When the estimator node does not receive data from the sensor node, the sensor value is known to be in a (−δi,+δi) interval from the last transmitted sensor value. This implicit information has been used to improve estimation performance in previous studies. The main contribution of this paper is to propose an algorithm, where the sensor value interval is reduced to (−δi/2,+δi/2) in certain situations. Thus, the proposed algorithm improves the overall estimation performance without any changes in the send-on-delta algorithms of the sensor nodes. Through numerical simulations, we demonstrate the feasibility and the usefulness of the proposed method.

  8. Performance Analysis of OCDMA Based on AND Detection in FTTH Access Network Using PIN & APD Photodiodes

    NASA Astrophysics Data System (ADS)

    Aldouri, Muthana; Aljunid, S. A.; Ahmad, R. Badlishah; Fadhil, Hilal A.

    2011-06-01

    In order to comprise between PIN photo detector and avalanche photodiodes in a system used double weight (DW) code to be a performance of the optical spectrum CDMA in FTTH network with point-to-multi-point (P2MP) application. The performance of PIN against APD is compared through simulation by using opt system software version 7. In this paper we used two networks designed as follows one used PIN photo detector and the second using APD photo diode, both two system using with and without erbium doped fiber amplifier (EDFA). It is found that APD photo diode in this system is better than PIN photo detector for all simulation results. The conversion used a Mach-Zehnder interferometer (MZI) wavelength converter. Also we are study, the proposing a detection scheme known as AND subtraction detection technique implemented with fiber Bragg Grating (FBG) act as encoder and decoder. This FBG is used to encode and decode the spectral amplitude coding namely double weight (DW) code in Optical Code Division Multiple Access (OCDMA). The performances are characterized through bit error rate (BER) and bit rate (BR) also the received power at various bit rate.

  9. Resting spontaneous activity in the default mode network predicts performance decline during prolonged attention workload.

    PubMed

    Gui, Danyang; Xu, Sihua; Zhu, Senhua; Fang, Zhuo; Spaeth, Andrea M; Xin, Yuanyuan; Feng, Tingyong; Rao, Hengyi

    2015-10-15

    After continuous and prolonged cognitive workload, people typically show reduced behavioral performance and increased feelings of fatigue, which are known as "time-on-task (TOT) effects". Although TOT effects are pervasive in modern life, their underlying neural mechanisms remain elusive. In this study, we induced TOT effects by administering a 20-min continuous psychomotor vigilance test (PVT) to a group of 16 healthy adults and used resting-state blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to examine spontaneous brain activity changes associated with fatigue and performance. Behaviorally, subjects displayed robust TOT effects, as reflected by increasingly slower reaction times as the test progressed and higher self-reported mental fatigue ratings after the 20-min PVT. Compared to pre-test measurements, subjects exhibited reduced amplitudes of low-frequency fluctuation (ALFF) in the default mode network (DMN) and increased ALFF in the thalamus after the test. Subjects also exhibited reduced anti-correlations between the posterior cingulate cortex (PCC) and right middle prefrontal cortex after the test. Moreover, pre-test resting ALFF in the PCC and medial prefrontal cortex (MePFC) predicted subjects' subsequent performance decline; individuals with higher ALFF in these regions exhibited more stable reaction times throughout the 20-min PVT. These results support the important role of both task-positive and task-negative networks in mediating TOT effects and suggest that spontaneous activity measured by resting-state BOLD fMRI may be a marker of mental fatigue.

  10. Optical node for fast packet-switching networks in the KEOPS project: structure and performance aspects

    NASA Astrophysics Data System (ADS)

    Chiaroni, Dominique; Lavigne, Bruno; Tran, Tri; Hamon, Laure; Jourdan, Amaury

    1998-10-01

    The future telecommunication network will have to face the dramatic increase of subscribers as well as the increase of the user bandwidth through new services. All-optical packet switching techniques can become a strategic objective to offer on an unique technology a service-transparent network. In this paper, we will describe in detail the structure of an optical packet switching node developed in the framework of the ACTS 043 KEOPS project. An analysis of the key functions will be reported to fulfill system requirements including cascadability. In particular the input synchronization, the Broadcast-and-select switching matrix and the output regenerative interface will be described and physical performance will be assessed through theoretical analysis: quality of the signal, packet jitter and packet power fluctuation. The electronic circuitry for the control of the components of each sub-block will be described. Finally, experimental validations of a 160 Gbit/s throughput node will be reported. In order to complete the analysis, the logical performance in a Bernoulli-type traffic will be regarded. In particular an optimized buffer including a recirculation loop will be studied. Logical performance exhibiting a packet loss rate lower than 10-9 for a 0.8 load and mean packet delay as low as 3 packet slots will be illustrated, thereby demonstrating full compatibility with ATM constraints. Finally, new perspectives in terms of throughput potential through cascading will be drawn.

  11. Improving the Performance of the Structure-Based Connectionist Network for Diagnosis of Helicopter Gearboxes

    NASA Technical Reports Server (NTRS)

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

    1996-01-01

    A diagnostic method is introduced for helicopter gearboxes that uses knowledge of the gear-box structure and characteristics of the 'features' of vibration to define the influences of faults on features. The 'structural influences' in this method are defined based on the root mean square value of vibration obtained from a simplified lumped-mass model of the gearbox. The structural influences are then converted to fuzzy variables, to account for the approximate nature of the lumped-mass model, and used as the weights of a connectionist network. Diagnosis in this Structure-Based Connectionist Network (SBCN) is performed by propagating the abnormal vibration features through the weights of SBCN to obtain fault possibility values for each component in the gearbox. Upon occurrence of misdiagnoses, the SBCN also has the ability to improve its diagnostic performance. For this, a supervised training method is presented which adapts the weights of SBCN to minimize the number of misdiagnoses. For experimental evaluation of the SBCN, vibration data from a OH-58A helicopter gearbox collected at NASA Lewis Research Center is used. Diagnostic results indicate that the SBCN is able to diagnose about 80% of the faults without training, and is able to improve its performance to nearly 100% after training.

  12. Comparison of High Performance Network Options: EDR InfiniBand vs.100Gb RDMA Capable Ethernet

    SciTech Connect

    Kachelmeier, Luke Anthony; Van Wig, Faith Virginia; Erickson, Kari Natania

    2016-08-08

    These are the slides for a presentation at the HPC Mini Showcase. This is a comparison of two different high performance network options: EDR InfiniBand and 100Gb RDMA capable ethernet. The conclusion of this comparison is the following: there is good potential, as shown with the direct results; 100Gb technology is too new and not standardized, thus deployment effort is complex for both options; different companies are not necessarily compatible; if you want 100Gb/s, you must get it all from one place.

  13. Prediction of the outage performance of a microwave multiple-hop network due to rain attenuation

    NASA Astrophysics Data System (ADS)

    Kanellopoulos, John D.; Gakis, Lampros

    1987-10-01

    In the design of tandem links using frequencies above 10 GHz, it is necessary to estimate outage time occurrence probability due to rain attenuation. Subject of this paper is the theoretical analysis of simultaneous probability of rain attenuation for tendem links by studying the joint distribution of correlated lognormal variables. This analysis is appropriate to locations where the point rainrate distribution approximates the lognormal function. The theoretical predictions for the outage performance of the multiple-hop network have been compared with existing experimental data for tandem links located in France, USA and Japan. The agreement has been found to be encouraging.

  14. [Improved learning capacity and discrimination performance of neural networks in pattern recognition of biosignals].

    PubMed

    Herrmann, L; Rienäcker, U

    1992-04-01

    Pattern recognition was an important goal in the early work on artificial neural networks. Without arousing dramatic speculation, the paper describes, how a "natural" method of dealing with the configuration of the input layer can considerably improve learning behaviour and classification rate of a modified multi-layered perception with backpropagation of the error learning rule. Using this method, recognition of complex patterns in electrophysiological signals can be performed more accurately, without rules or complicated heuristic procedures. The proposed technique is demonstrated using recognition of the J-point in the ECG as an example.

  15. Computing by Robust Transience: How the Fronto-Parietal Network Performs Sequential, Category-Based Decisions.

    PubMed

    Chaisangmongkon, Warasinee; Swaminathan, Sruthi K; Freedman, David J; Wang, Xiao-Jing

    2017-03-22

    Decision making involves dynamic interplay between internal judgements and external perception, which has been investigated in delayed match-to-category (DMC) experiments. Our analysis of neural recordings shows that, during DMC tasks, LIP and PFC neurons demonstrate mixed, time-varying, and heterogeneous selectivity, but previous theoretical work has not established the link between these neural characteristics and population-level computations. We trained a recurrent network model to perform DMC tasks and found that the model can remarkably reproduce key features of neuronal selectivity at the single-neuron and population levels. Analysis of the trained networks elucidates that robust transient trajectories of the neural population are the key driver of sequential categorical decisions. The directions of trajectories are governed by network self-organized connectivity, defining a "neural landscape" consisting of a task-tailored arrangement of slow states and dynamical tunnels. With this model, we can identify functionally relevant circuit motifs and generalize the framework to solve other categorization tasks.

  16. Performance Evaluation of Multi-Channel Wireless Mesh Networks with Embedded Systems

    PubMed Central

    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). PMID:22368482

  17. Acquisition and performance of delayed-response tasks: a neural network model.

    PubMed

    Gisiger, Thomas; Kerszberg, Michel; Changeux, Jean-Pierre

    2005-05-01

    We study the time evolution of a neural network model as it learns the three stages of a visual delayed-matching-to-sample (DMS) task: identification of the sample, retention during delay, and matching of sample and target, ignoring distractors. We introduce a neurobiologically plausible, uncommitted architecture, comprising an "executive" subnetwork gating connections to and from a "working" layer. The network learns DMS by reinforcement: reward-dependent synaptic plasticity generates task-dependent behaviour. During learning, working layer cells exhibit stimulus specialization and increased tuning of their firing. The emergence of top-down activity is observed, reproducing aspects of prefrontal cortex control on activity in the visual areas of inferior temporal cortex. We observe a lability of neural systems during learning, with a tendency to encode spurious associations. Executive areas are instrumental during learning to prevent such associations; they are also fundamental for the "mature" network to keep passing DMS. In the mature model, the working layer functions as a short-term memory. The mature system is remarkably robust against cell damage and its performance degrades gracefully as damage increases. The model underlines that executive systems, which regulate the flow of information between working memory and sensory areas, are required for passing tests such as DMS. At the behavioural level, the model makes testable predictions about the errors expected from subjects learning the DMS.

  18. 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).

  19. Hellenic Unified Seismological Network: an evaluation of its performance through SNES method

    NASA Astrophysics Data System (ADS)

    D'Alessandro, Antonino; Papanastassiou, Dimitris; Baskoutas, Ioannis

    2011-06-01

    In this paper, we analyse the location performance of the Hellenic (Greek) Unified Seismological Network (HUSN) by Seismic Network Evaluation through Simulation method (SNES). This method gives, as a function of magnitude, hypocentral depth and confidence level, the spatial distribution of the: number of active stations in the location procedure and their relative azimuthal gaps and confidence intervals in hypocentral parameters regarding both the geometry of the seismic network and the use of an inadequate velocity model. Greece is located on a tectonically active plate boundary at the convergence of the Eurasian and African lithospheric plates and exhibits a high level of seismicity. The HUSN monitors the seismicity in Greek territory from 2007. At present it is composed by 88 seismic stations appropriately distribute in the area of Greece. The application of the SNES method permitted us to evaluate the background noise levels recorded by the network stations and estimate an empirical law that links the variance of P and S traveltime residuals to hypocentral distance. The statistical analysis of the P and S traveltime residuals allowed us to assess the appropriateness of the velocity model used by the HUSN in the location routine process. We constructed SNES maps for magnitudes (?) of 2, 2.5 and 3, fixing the hypocentral depth to 10 km and the confidence level to 95 per cent. We also investigated, by two different vertical sections, the behaviour of the errors in hypocentral parameters estimates as function of depth. Finally, we also evaluated, fixing the hypocentral depth to 10 km and the confidence level to 95 per cent, the Magnitude of Completeness. Through the application of the SNES method, we demonstrate that the HUSN provides the best monitoring coverage in western Greece with errors, that for ? = 2.5, are less than 2 and 5 km for epicentre and hypocentral depth, respectively. At magnitude 2.5, this seismic network is capable of constraining earthquake

  20. High-Bandwidth Tactical-Network Data Analysis in a High-Performance-Computing (HPC) Environment: Introduction

    DTIC Science & Technology

    2015-09-01

    ARL-TR-7409 ● SEP 2015 US Army Research Laboratory High-Bandwidth Tactical-Network Data Analysis in a High-Performance-Computing...TR-7409 ● SEP 2015 US Army Research Laboratory High-Bandwidth Tactical-Network Data Analysis in a High-Performance-Computing (HPC...information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering

  1. Optimizing the Reliability and Performance of Service Composition Applications with Fault Tolerance in Wireless Sensor Networks.

    PubMed

    Wu, Zhao; Xiong, Naixue; Huang, Yannong; Xu, Degang; Hu, Chunyang

    2015-11-06

    The services composition technology provides flexible methods for building service composition applications (SCAs) in wireless sensor networks (WSNs). The high reliability and high performance of SCAs help services composition technology promote the practical application of WSNs. The optimization methods for reliability and performance used for traditional software systems are mostly based on the instantiations of software components, which are inapplicable and inefficient in the ever-changing SCAs in WSNs. In this paper, we consider the SCAs with fault tolerance in WSNs. Based on a Universal Generating Function (UGF) we propose a reliability and performance model of SCAs in WSNs, which generalizes a redundancy optimization problem to a multi-state system. Based on this model, an efficient optimization algorithm for reliability and performance of SCAs in WSNs is developed based on a Genetic Algorithm (GA) to find the optimal structure of SCAs with fault-tolerance in WSNs. In order to examine the feasibility of our algorithm, we have evaluated the performance. Furthermore, the interrelationships between the reliability, performance and cost are investigated. In addition, a distinct approach to determine the most suitable parameters in the suggested algorithm is proposed.

  2. Optimizing the Reliability and Performance of Service Composition Applications with Fault Tolerance in Wireless Sensor Networks

    PubMed Central

    Wu, Zhao; Xiong, Naixue; Huang, Yannong; Xu, Degang; Hu, Chunyang

    2015-01-01

    The services composition technology provides flexible methods for building service composition applications (SCAs) in wireless sensor networks (WSNs). The high reliability and high performance of SCAs help services composition technology promote the practical application of WSNs. The optimization methods for reliability and performance used for traditional software systems are mostly based on the instantiations of software components, which are inapplicable and inefficient in the ever-changing SCAs in WSNs. In this paper, we consider the SCAs with fault tolerance in WSNs. Based on a Universal Generating Function (UGF) we propose a reliability and performance model of SCAs in WSNs, which generalizes a redundancy optimization problem to a multi-state system. Based on this model, an efficient optimization algorithm for reliability and performance of SCAs in WSNs is developed based on a Genetic Algorithm (GA) to find the optimal structure of SCAs with fault-tolerance in WSNs. In order to examine the feasibility of our algorithm, we have evaluated the performance. Furthermore, the interrelationships between the reliability, performance and cost are investigated. In addition, a distinct approach to determine the most suitable parameters in the suggested algorithm is proposed. PMID:26561818

  3. Communication performance analysis and comparison of two patterns for data exchange between nodes in WorldFIP fieldbus network.

    PubMed

    Liang, Geng; Wang, Hong; Li, Wen; Li, Dazhong

    2010-10-01

    Data exchange patterns between nodes in WorldFIP fieldbus network are quite important and meaningful in improving the communication performance of WorldFIP network. Based on the basic communication ways supported in WorldFIP protocol, we propose two patterns for implementation of data exchange between peer nodes over WorldFIP network. Effects on communication performance of WorldFIP network in terms of some network parameters, such as number of bytes in user's data and turn-around time, in both the proposed patterns, are analyzed at length when different network speeds are applied. Such effects with the patterns of periodic message transmission using acknowledged and non-acknowledged messages, are also studied. Communication performance in both the proposed patterns are analyzed and compared. Practical applications of the research are presented. Through the study, it can be seen that different data exchange patterns make a great difference in improving communication efficiency with different network parameters, which is quite useful and helpful in the practical design of distributed systems based on WorldFIP network.

  4. Baseline Physical Performance, Health, and Functioning of Participants in the Frequent Hemodialysis Network (FHN) Trial

    PubMed Central

    Kaysen, George A.; Larive, Brett; Painter, Patricia; Craig, Alexander; Lindsay, Robert M.; Rocco, Michael V.; Daugirdas, John T.; Schulman, Gerald; Chertow, Glenn M.

    2010-01-01

    Background Self-reported physical health and functioning and direct measures of physical performance are decreased in hemodialysis patients and are associated with mortality and hospitalization. Study Design We determined baseline cross-sectional associations of physical performance, health, and functioning with demographics, clinical characteristics, nutritional indexes, laboratory benchmarks, and measures of body composition in participants in the Frequent Hemodialysis Network (FHN) trial. Setting & Participants 375 persons enrolled in the FHN with data for physical performance, health, and functioning. Predictors Explanatory variables were categorized into fixed factors of age, race, comorbid conditions (diabetes mellitus, heart failure, and peripheral arterial disease) and potentially modifiable factors of dialysis dose, phosphorus level, hemoglobin level, equilibrated normalized protein catabolic rate (enPCR), body composition, body mass index, phase angle, and ratio of intracellular water volume to body weight (calculated from bioelectrical impedance). Outcomes Scores on tests of physical performance, health, and functioning. Measurements Physical performance measured using the Short Physical Performance Battery, self-reported physical health and functioning using the 36-Item Short Form Health Survey (SF-36). Body composition (body mass index and bioimpedance analysis) and laboratory data were obtained from affiliated dialysis providers. Results Relative to population norms, scores for all 3 physicality metrics were low. Poorer scores on all 3 metrics were associated with diabetes mellitus and peripheral arterial disease. Poorer scores on the SF-36 Physical Functioning subscale and Short Physical Performance Battery also were associated with age, lower ratio of intracellular water volume to body weight, and lower enPCR. Black race was associated with poorer scores on the Short Physical Performance Battery. Limitations This was a cross-sectional study of

  5. Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance Improvement

    PubMed Central

    Negri, Lucas; Nied, Ademir; Kalinowski, Hypolito; Paterno, Aleksander

    2011-01-01

    This paper presents a benchmark for peak detection algorithms employed in fiber Bragg grating spectrometric interrogation systems. The accuracy, precision, and computational performance of currently used algorithms and those of a new proposed artificial neural network algorithm are compared. Centroid and gaussian fitting algorithms are shown to have the highest precision but produce systematic errors that depend on the FBG refractive index modulation profile. The proposed neural network displays relatively good precision with reduced systematic errors and improved computational performance when compared to other networks. Additionally, suitable algorithms may be chosen with the general guidelines presented. PMID:22163806

  6. Partial least squares regression, support vector machine regression, and transcriptome-based distances for prediction of maize hybrid performance with gene expression data.

    PubMed

    Fu, Junjie; Falke, K Christin; Thiemann, Alexander; Schrag, Tobias A; Melchinger, Albrecht E; Scholten, Stefan; Frisch, Matthias

    2012-03-01

    The performance of hybrids can be predicted with gene expression data from their parental inbred lines. Implementing such prediction approaches in breeding programs promises to increase the efficiency of hybrid breeding. The objectives of our study were to compare the accuracy of prediction models employing multiple linear regression (MLR), partial least squares regression (PLS), support vector machine regression (SVM), and transcriptome-based distances (D(B)). For a factorial of 7 flint and 14 dent maize lines, the grain yield of the hybrids was assessed and the gene expression of the parental lines was profiled with a 56k microarray. The accuracy of the prediction models was measured by the correlation between predicted and observed yield employing two cross-validation schemes. The first modeled the prediction of hybrids when testcross data are available for both parental lines (type 2 hybrids), and the second modeled the prediction of hybrids when no testcross data for the parental lines were available (type 0 hybrids). MLR, SVM, and PLS resulted in a high correlation between predicted and observed yield for type 2 hybrids, whereas for type 0 hybrids D(B) had greater prediction accuracy. The regression methods were robust to the choice of the set of profiled genes and required only a few hundred genes. In contrast, for an accurate hybrid prediction with D(B), 1,000-1,500 genes were required, and the prediction accuracy depended strongly on the set of profiled genes. We conclude that for prediction within one set of genetic material MLR is a promising approach, and for transferring prediction models from one set of genetic material to a related one, the transcriptome-based distance D(B) is most promising.

  7. Utah's Regional/Urban ANSS Seismic Network---Strategies and Tools for Quality Performance

    NASA Astrophysics Data System (ADS)

    Burlacu, R.; Arabasz, W. J.; Pankow, K. L.; Pechmann, J. C.; Drobeck, D. L.; Moeinvaziri, A.; Roberson, P. M.; Rusho, J. A.

    2007-05-01

    The University of Utah's regional/urban seismic network (224 stations recorded: 39 broadband, 87 strong-motion, 98 short-period) has become a model for locally implementing the Advanced National Seismic System (ANSS) because of successes in integrating weak- and strong-motion recording and in developing an effective real-time earthquake information system. Early achievements included implementing ShakeMap, ShakeCast, point-to- multipoint digital telemetry, and an Earthworm Oracle database, as well as in-situ calibration of all broadband and strong-motion stations and submission of all data and metadata into the IRIS DMC. Regarding quality performance, our experience as a medium-size regional network affirms the fundamental importance of basics such as the following: for data acquisition, deliberate attention to high-quality field installations, signal quality, and computer operations; for operational efficiency, a consistent focus on professional project management and human resources; and for customer service, healthy partnerships---including constant interactions with emergency managers, engineers, public policy-makers, and other stakeholders as part of an effective state earthquake program. (Operational cost efficiencies almost invariably involve trade-offs between personnel costs and the quality of hardware and software.) Software tools that we currently rely on for quality performance include those developed by UUSS (e.g., SAC and shell scripts for estimating local magnitudes) and software developed by other organizations such as: USGS (Earthworm), University of Washington (interactive analysis software), ISTI (SeisNetWatch), and IRIS (PDCC, BUD tools). Although there are many pieces, there is little integration. One of the main challenges we face is the availability of a complete and coherent set of tools for automatic and post-processing to assist in achieving the goals/requirements set forth by ANSS. Taking our own network---and ANSS---to the next level

  8. Performance Analysis of Integrated Wireless Sensor and Multibeam Satellite Networks Under Terrestrial Interference

    PubMed Central

    Li, Hongjun; Yin, Hao; Gong, Xiangwu; Dong, Feihong; Ren, Baoquan; He, Yuanzhi; Wang, Jingchao

    2016-01-01

    This paper investigates the performance of integrated wireless sensor and multibeam satellite networks (IWSMSNs) under terrestrial interference. The IWSMSNs constitute sensor nodes (SNs), satellite sinks (SSs), multibeam satellite and remote monitoring hosts (RMHs). The multibeam satellite covers multiple beams and multiple SSs in each beam. The SSs can be directly used as SNs to transmit sensing data to RMHs via the satellite, and they can also be used to collect the sensing data from other SNs to transmit to the RMHs. We propose the hybrid one-dimensional (1D) and 2D beam models including the equivalent intra-beam interference factor β from terrestrial communication networks (TCNs) and the equivalent inter-beam interference factor α from adjacent beams. The terrestrial interference is possibly due to the signals from the TCNs or the signals of sinks being transmitted to other satellite networks. The closed-form approximations of capacity per beam are derived for the return link of IWSMSNs under terrestrial interference by using the Haar approximations where the IWSMSNs experience the Rician fading channel. The optimal joint decoding capacity can be considered as the upper bound where all of the SSs’ signals can be jointly decoded by a super-receiver on board the multibeam satellite or a gateway station that knows all of the code books. While the linear minimum mean square error (MMSE) capacity is where all of the signals of SSs are decoded singularly by a multibeam satellite or a gateway station. The simulations show that the optimal capacities are obviously higher than the MMSE capacities under the same conditions, while the capacities are lowered by Rician fading and converge as the Rician factor increases. α and β jointly affect the performance of hybrid 1D and 2D beam models, and the number of SSs also contributes different effects on the optimal capacity and MMSE capacity of the IWSMSNs. PMID:27754438

  9. Performance Analysis of Integrated Wireless Sensor and Multibeam Satellite Networks Under Terrestrial Interference.

    PubMed

    Li, Hongjun; Yin, Hao; Gong, Xiangwu; Dong, Feihong; Ren, Baoquan; He, Yuanzhi; Wang, Jingchao

    2016-10-14

    This paper investigates the performance of integrated wireless sensor and multibeam satellite networks (IWSMSNs) under terrestrial interference. The IWSMSNs constitute sensor nodes (SNs), satellite sinks (SSs), multibeam satellite and remote monitoring hosts (RMHs). The multibeam satellite covers multiple beams and multiple SSs in each beam. The SSs can be directly used as SNs to transmit sensing data to RMHs via the satellite, and they can also be used to collect the sensing data from other SNs to transmit to the RMHs. We propose the hybrid one-dimensional (1D) and 2D beam models including the equivalent intra-beam interference factor β from terrestrial communication networks (TCNs) and the equivalent inter-beam interference factor α from adjacent beams. The terrestrial interference is possibly due to the signals from the TCNs or the signals of sinks being transmitted to other satellite networks. The closed-form approximations of capacity per beam are derived for the return link of IWSMSNs under terrestrial interference by using the Haar approximations where the IWSMSNs experience the Rician fading channel. The optimal joint decoding capacity can be considered as the upper bound where all of the SSs' signals can be jointly decoded by a super-receiver on board the multibeam satellite or a gateway station that knows all of the code books. While the linear minimum mean square error (MMSE) capacity is where all of the signals of SSs are decoded singularly by a multibeam satellite or a gateway station. The simulations show that the optimal capacities are obviously higher than the MMSE capacities under the same conditions, while the capacities are lowered by Rician fading and converge as the Rician factor increases. α and β jointly affect the performance of hybrid 1D and 2D beam models, and the number of SSs also contributes different effects on the optimal capacity and MMSE capacity of the IWSMSNs.

  10. Performance evaluation of a Wireless Body Area sensor network for remote patient monitoring.

    PubMed

    Khan, Jamil Y; Yuce, Mehmet R; Karami, Farbood

    2008-01-01

    In recent years, interests in the application of Wireless Body Area Network (WBAN) have grown considerably. A WBAN can be used to develop a patient monitoring system which offers flexibility and mobility to patients. Use of a WBAN will also allow the flexibility of setting up a remote monitoring system via either the internet or an intranet. For such medical systems it is very important that a WBAN can collect and transmit data reliably, and in a timely manner to the monitoring entity. In this paper we examine the performance of an IEEE802.15.4/Zigbee MAC based WBAN operating in different patient monitoring environment. We study the performance of a remote patient monitoring system using an OPNET based simulation model.

  11. Performance-oriented antiwindup for a class of linear control systems with augmented neural network controller.

    PubMed

    Herrmann, Guido; Turner, Matthew C; Postlethwaite, Ian

    2007-03-01

    This paper presents a conditioning scheme for a linear control system which is enhanced by a neural network (NN) controller and subjected to a control signal amplitude limit. The NN controller improves the performance of the linear control system by directly estimating an actuator-matched, unmodeled, nonlinear disturbance, in closed-loop, and compensating for it. As disturbances are generally known to be bounded, the nominal NN-control element is modified to keep its output below the disturbance bound. The linear control element is conditioned by an antiwindup (AW) compensator which ensures performance close to the nominal controller and swift recovery from saturation. For this, the AW compensator proposed is of low order, designed using convex linear matrix inequalities (LMIs) optimization.

  12. Cross-linked carbon networks constructed from N-doped nanosheets with enhanced performance for supercapacitors

    NASA Astrophysics Data System (ADS)

    Liu, Qingqing; Zhong, Jialiang; Sun, Zhipeng; Mi, Hongyu

    2017-02-01

    Hierarchically porous carbons offer great benefits for constructing advanced electrodes for energy-related applications. Herein, we reported facile synthesis of cross-linked carbon networks (HPCNs) made from N-doped nanosheets. By using MgO as self-sacrificial templates, the polyethylene glycol and melamine precursors were first uniformly coated on the template, and then annealed at the elevated temperature in inert atmosphere before removing the templates by mild acid etching. Interestingly, the capacitance performance of HPCNs could be easily modulated by adjusting the mass ratio of the precursors and templates, as well as the carbonization temperature. The optimized HPCNs showed specific capacitances of 192.6 F g-1 at 1.0 A g-1 and 156.2 F g-1 even at 20 A g-1 in 6.0 M KOH solution, and long-term cyclability with 85.5% capacitance retention at high current load of 10 A g-1 after 8000 successive cycles, which were attributed to structural merits of these continuous networks including high surface area of 370.8 m2 g-1, high pore volume of 1.65 cm3 g-1, as well as high nitrogen content of 9.920 wt.%. Owing to simplicity of the synthesis method and superior performance, such HPCNs may promise great potential in energy storage fields.

  13. Leasing-Based Performance Analysis in Energy Harvesting Cognitive Radio Networks.

    PubMed

    Zeng, Fanzi; Xu, Jisheng

    2016-02-27

    In this paper, we consider an energy harvesting cognitive radio network (CRN), where both of primary user (PU) and secondary user (SU) are operating in time slotted mode, and the SU powered exclusively by the energy harvested from the radio signal of the PU. The SU can only perform either energy harvesting or data transmission due to the hardware limitation. In this case, the entire time-slot is segmented into two non-overlapping fractions. During the first sub-timeslot, the SU can harvest energy from the ambient radio signal when the PU is transmitting. In order to obtain more revenue, the PU leases a portion of its time to SU, while the SU can transmit its own data by using the harvested energy. According to convex optimization, we get the optimal leasing time to maximize the SU's throughput while guaranteeing the quality of service (QoS) of PU. To evaluate the performance of our proposed spectrum leasing scheme, we compare the utility of PU and the energy efficiency ratio of the entire networks in our framework with the conventional strategies respectively. The numerical simulation results prove the superiority of our proposed spectrum leasing scheme.

  14. Leasing-Based Performance Analysis in Energy Harvesting Cognitive Radio Networks

    PubMed Central

    Zeng, Fanzi; Xu, Jisheng

    2016-01-01

    In this paper, we consider an energy harvesting cognitive radio network (CRN), where both of primary user (PU) and secondary user (SU) are operating in time slotted mode, and the SU powered exclusively by the energy harvested from the radio signal of the PU. The SU can only perform either energy harvesting or data transmission due to the hardware limitation. In this case, the entire time-slot is segmented into two non-overlapping fractions. During the first sub-timeslot, the SU can harvest energy from the ambient radio signal when the PU is transmitting. In order to obtain more revenue, the PU leases a portion of its time to SU, while the SU can transmit its own data by using the harvested energy. According to convex optimization, we get the optimal leasing time to maximize the SU’s throughput while guaranteeing the quality of service (QoS) of PU. To evaluate the performance of our proposed spectrum leasing scheme, we compare the utility of PU and the energy efficiency ratio of the entire networks in our framework with the conventional strategies respectively. The numerical simulation results prove the superiority of our proposed spectrum leasing scheme. PMID:26927131

  15. Using multi-class queuing network to solve performance models of e-business sites.

    PubMed

    Zheng, Xiao-ying; Chen, De-ren

    2004-01-01

    Due to e-business's variety of customers with different navigational patterns and demands, multi-class queuing network is a natural performance model for it. The open multi-class queuing network(QN) models are based on the assumption that no service center is saturated as a result of the combined loads of all the classes. Several formulas are used to calculate performance measures, including throughput, residence time, queue length, response time and the average number of requests. The solution technique of closed multi-class QN models is an approximate mean value analysis algorithm (MVA) based on three key equations, because the exact algorithm needs huge time and space requirement. As mixed multi-class QN models, include some open and some closed classes, the open classes should be eliminated to create a closed multi-class QN so that the closed model algorithm can be applied. Some corresponding examples are given to show how to apply the algorithms mentioned in this article. These examples indicate that multi-class QN is a reasonably accurate model of e-business and can be solved efficiently.

  16. Supramolecular Polymer Network-Mediated Self-Assembly of Semicrystalline Polymers with Excellent Crystalline Performance.

    PubMed

    Cheng, Chih-Chia; Chuang, Wei-Tsung; Lee, Duu-Jong; Xin, Zhong; Chiu, Chih-Wei

    2017-03-01

    A novel application of supramolecular interactions within semicrystalline polymers, capable of self-assembling into supramolecular polymer networks via self-complementary multiple hydrogen-bonded complexes, is demonstrated for efficient construction of highly controlled self-organizing hierarchical structures to offer a direct, efficient nucleation pathway resulting in superior crystallization performance. Herein, a novel functionalized poly(ε-caprolactone) containing self-complementary sextuple hydrogen-bonded uracil-diamidopyridine (U-DPy) moieties is successfully developed and demonstrated excellent thermal and viscoelastic properties as well as high dynamic structural stability in the bulk state due to physical cross-linking created by reversible sextuple hydrogen bonding between U-DPy units. Due to the ability to vary the extent of the reversible network by tuning the U-DPy content, this newly developed material can be readily adjusted to obtain the desired crystalline products with specific characteristics. Importantly, incorporating only 0.1% U-DPy resulted in a polymer with a high crystallization rate constant, short crystallization half-time, and much more rapid crystallization kinetics than pristine PCL, indicating a low content of U-DPy moieties provides highly efficient nucleation sites that manipulate the nucleation and growth processes of polymer crystals to promote crystallization and chain alignment in bulk. This new system is suggested as a potential new route to substantially improve the performance of polymer crystallization.

  17. Performance Analysis of Physical Layer Security of Opportunistic Scheduling in Multiuser Multirelay Cooperative Networks.

    PubMed

    Shim, Kyusung; Do, Nhu Tri; An, Beongku

    2017-02-15

    In this paper, we study the physical layer security (PLS) of opportunistic scheduling for uplink scenarios of multiuser multirelay cooperative networks. To this end, we propose a low-complexity, yet comparable secrecy performance source relay selection scheme, called the proposed source relay selection (PSRS) scheme. Specifically, the PSRS scheme first selects the least vulnerable source and then selects the relay that maximizes the system secrecy capacity for the given selected source. Additionally, the maximal ratio combining (MRC) technique and the selection combining (SC) technique are considered at the eavesdropper, respectively. Investigating the system performance in terms of secrecy outage probability (SOP), closed-form expressions of the SOP are derived. The developed analysis is corroborated through Monte Carlo simulation. Numerical results show that the PSRS scheme significantly improves the secure ability of the system compared to that of the random source relay selection scheme, but does not outperform the optimal joint source relay selection (OJSRS) scheme. However, the PSRS scheme drastically reduces the required amount of channel state information (CSI) estimations compared to that required by the OJSRS scheme, specially in dense cooperative networks.

  18. Using artificial neural networks to predict the quality and performance of oilfield cements

    SciTech Connect

    Coveney, P.V.; Hughes, T.L.; Fletcher, P.

    1996-12-31

    Inherent batch to batch variability, ageing and contamination are major factors contributing to variability in oilfield cement slurry performance. Of particular concern are problems encountered when a slurry is formulated with one cement sample and used with a batch having different properties. Such variability imposes a heavy burden on performance testing and is often a major factor in operational failure. We describe methods which allow the identification, characterization and prediction of the variability of oilfield cements. Our approach involves predicting cement compositions, particle size distributions and thickening time curves from the diffuse reflectance infrared Fourier transform spectrum of neat cement powders. Predictions make use of artificial neural networks. Slurry formulation thickening times can be predicted with uncertainties of less than {+-}10%. Composition and particle size distributions can be predicted with uncertainties a little greater than measurement error but general trends and differences between cements can be determined reliably. Our research shows that many key cement properties are captured within the Fourier transform infrared spectra of cement powders and can be predicted from these spectra using suitable neural network techniques. Several case studies are given to emphasize the use of these techniques which provide the basis for a valuable quality control tool now finding commercial use in the oilfield.

  19. Performance Analysis of Physical Layer Security of Opportunistic Scheduling in Multiuser Multirelay Cooperative Networks

    PubMed Central

    Shim, Kyusung; Do, Nhu Tri; An, Beongku

    2017-01-01

    In this paper, we study the physical layer security (PLS) of opportunistic scheduling for uplink scenarios of multiuser multirelay cooperative networks. To this end, we propose a low-complexity, yet comparable secrecy performance source relay selection scheme, called the proposed source relay selection (PSRS) scheme. Specifically, the PSRS scheme first selects the least vulnerable source and then selects the relay that maximizes the system secrecy capacity for the given selected source. Additionally, the maximal ratio combining (MRC) technique and the selection combining (SC) technique are considered at the eavesdropper, respectively. Investigating the system performance in terms of secrecy outage probability (SOP), closed-form expressions of the SOP are derived. The developed analysis is corroborated through Monte Carlo simulation. Numerical results show that the PSRS scheme significantly improves the secure ability of the system compared to that of the random source relay selection scheme, but does not outperform the optimal joint source relay selection (OJSRS) scheme. However, the PSRS scheme drastically reduces the required amount of channel state information (CSI) estimations compared to that required by the OJSRS scheme, specially in dense cooperative networks. PMID:28212286

  20. A robust and high-performance queue management controller for large round trip time networks

    NASA Astrophysics Data System (ADS)

    Khoshnevisan, Ladan; Salmasi, Farzad R.

    2016-05-01

    Congestion management for transmission control protocol is of utmost importance to prevent packet loss within a network. This necessitates strategies for active queue management. The most applied active queue management strategies have their inherent disadvantages which lead to suboptimal performance and even instability in the case of large round trip time and/or external disturbance. This paper presents an internal model control robust queue management scheme with two degrees of freedom in order to restrict the undesired effects of large and small round trip time and parameter variations in the queue management. Conventional approaches such as proportional integral and random early detection procedures lead to unstable behaviour due to large delay. Moreover, internal model control-Smith scheme suffers from large oscillations due to the large round trip time. On the other hand, other schemes such as internal model control-proportional integral and derivative show excessive sluggish performance for small round trip time values. To overcome these shortcomings, we introduce a system entailing two individual controllers for queue management and disturbance rejection, simultaneously. Simulation results based on Matlab/Simulink and also Network Simulator 2 (NS2) demonstrate the effectiveness of the procedure and verify the analytical approach.