Network Techniques (network + techniques)

Distribution by Scientific Domains

Kinds of Network Techniques

  • neural network techniques

  • Selected Abstracts

    An affordable modular mobile robotic platform with fuzzy logic control and evolutionary artificial neural networks

    Maurice Tedder
    Autonomous robotics projects encompass the rich nature of integrated systems that includes mechanical, electrical, and computational software components. The availability of smaller and cheaper hardware components has helped make possible a new dimension in operational autonomy. This paper describes a mobile robotic platform consisting of several integrated modules including a laptop computer that serves as the main control module, microcontroller-based motion control module, a vision processing module, a sensor interface module, and a navigation module. The laptop computer module contains the main software development environment with a user interface to access and control all other modules. Programming language independence is achieved by using standard input/output computer interfaces including RS-232 serial port, USB, networking, audio input and output, and parallel port devices. However, with the same hardware technology available to all, the distinguishing factor in most cases for intelligent systems becomes the software design. The software for autonomous robots must intelligently control the hardware so that it functions in unstructured, dynamic, and uncertain environments while maintaining an autonomous adaptability. This paper describes how we introduced fuzzy logic control to one robot platform in order to solve the 2003 Intelligent Ground Vehicle Competition (IGVC) Autonomous Challenge problem. This paper also describes the introduction of hybrid software design that utilizes Fuzzy Evolutionary Artificial Neural Network techniques. In this design, rather than using a control program that is directly coded, the robot's artificial neural net is first trained with a training data set using evolutionary optimization techniques to adjust weight values between neurons. The trained neural network with a weight average defuzzification method was able to make correct decisions to unseen vision patterns for the IGVC Autonomous Challenge. A comparison of the Lawrence Technological University robot designs and the design of the other competing schools shows that our platforms were the most affordable robot systems to use as tools for computer science and engineering education. © 2004 Wiley Periodicals, Inc. [source]

    Spatial prediction of nitrate pollution in groundwaters using neural networks and GIS: an application to South Rhodope aquifer (Thrace, Greece)

    Dr A. Gemitzi
    Abstract Neural network techniques combined with Geographical Information Systems (GIS), are used in the spatial prediction of nitrate pollution in groundwaters. Initially, the most important parameters controlling groundwater pollution by nitrates are determined. These include hydraulic conductivity of the aquifer, depth to the aquifer, land uses, soil permeability, and fine to coarse grain ratio in the unsaturated zone. All these parameters were quantified in a GIS environment, and were standardized in a common scale. Subsequently, a neural network classification was applied, using a multi-layer perceptron classifier with the back propagation (BP) algorithm, in order to categorize the examined area into categories of groundwater nitrate pollution potential. The methodology was applied to South Rhodope aquifer (Thrace, Greece). The calculation was based on information from 214 training sites, which correspond to monitored nitrate concentrations in groundwaters in the area. The predictive accuracy of the model developed reached 86% in the training samples, 74% in the overall sample and 71% in the test samples. This indicates that this methodology is promising to describe the spatial pattern of nitrate pollution. Copyright © 2008 John Wiley & Sons, Ltd. [source]

    Predicting summer rainfall in the Yangtze River basin with neural networks

    Heike Hartmann
    Abstract Summer rainfall in the Yangtze River basin is predicted using neural network techniques. Input variables (predictors) for the neural network are the Southern Oscillation Index (SOI), the East Atlantic/Western Russia (EA/WR) pattern, the Scandinavia (SCA) pattern, the Polar/Eurasia (POL) pattern and several indices calculated from sea surface temperatures (SST), sea level pressures (SLP) and snow data from December to April for the period from 1993 to 2002. The output variable of the neural network is rainfall from May to September for the period from 1994 to 2002, which was previously classified into six different regions by means of a principal component analysis (PCA). Rainfall is predicted from May to September 2002. The winter SST and SLP indices are identified to be the most important predictors of summer rainfall in the Yangtze River basin. The Tibetan Plateau snow depth, the SOI and the other teleconnection indices seem to be of minor importance for an accurate prediction. This may be the result of the length of the available time series, which does not allow a deeper analysis of the impact of multi-annual oscillations. The neural network algorithms proved to be capable of explaining most of the rainfall variability in the Yangtze River basin. For five out of six regions, our predictions explain at least 77% of the total variance of the measured rainfall. Copyright © 2007 Royal Meteorological Society [source]

    Managing QoS requirements for video streaming: from intra-node to inter-node

    Y. Bai
    Abstract Streaming video over IP networks has become increasingly popular; however, compared to traditional data traffic, video streaming places different demands on quality of service (QoS) in a network, particularly in terms of delay, delay variation, and data loss. In response to the QoS demands of video applications, network techniques have been proposed to provide QoS within a network. Unfortunately, while efficient from a network perspective, most existing solutions have not provided end-to-end QoS that is satisfactory to users. In this paper, packet scheduling and end-to-end QoS distribution schemes are proposed to address this issue. The design and implementation of the two schemes are based on the active networking paradigm. In active networks, routers can perform user-driven computation when forwarding packets, rather than just simple storing and forwarding packets, as in traditional networks. Both schemes thus take advantage of the capability of active networks enabling routers to adapt to the content of transmitted data and the QoS requirements of video users. In other words, packet scheduling at routers considers the correlation between video characteristics, available local resources and the resulting visual quality. The proposed QoS distribution scheme performs inter-node adaptation, dynamically adjusting local loss constraints in response to network conditions in order to satisfy the end-to-end loss requirements. An active network-based simulation shows that using QoS distribution and packet scheduling together increases the probability of meeting end-to-end QoS requirements of networked video. Copyright © 2005 John Wiley & Sons, Ltd. [source]

    A hybrid integral equation and neural network approach for fast extraction of frequency dependent parameters of multiconductor transmission lines

    G. Pan
    Abstract Multiconductor transmission lines (MTL) have been modeled by the distributed parameters R, L, C, and G in many commercial CAD packages, where most of the parameters are assumed to be frequency independent or at most . At gigahertz frequencies, such assumptions may introduce significantly large errors in the waveform simulation and timing. In this article, we present a new and fast technique based on a combination of neural network techniques and the integral equation method (IEM) to evaluate frequency dependences accurately, while dramatically reducing the computation time. © 2002 John Wiley & Sons, Inc. Int J RF and Microwave CAE 12: 37,50, 2002. [source]

    Robust Neural Network Controller Design For A Biaxial Servo System

    Chih-Hsien Yu
    ABSTRACT A robust control method for synchronizing a biaxial servo system motion is proposed in this paper. A new neural network based cross-coupled control and neural network techniques are used together to cancel out the skew error. In the proposed control scheme, the conventional fixed gain PID cross-coupled controller (PIDCCC) is replaced with the neural network cross-coupled controller (NNCCC) to maintain biaxial servo system synchronization motion. In addition, neural network PID position velocity and velocity controllers provide the necessary control actions to maintain synchronization while following a variable command trajectory. This scheme provides strong robustness with respect to uncertain dynamics and nonlinearities. The simulation results reveal that the proposed control structure adapts to a wide range of operating conditions and provides promising results under parameter variations and load changes. [source]

    Patient gender and radiopharmaceutical tracer is of minor importance for the interpretation of myocardial perfusion images using an artificial neural network

    Kristina Tägil
    Summary The purpose of this study was to assess the influence of patient gender and choice of perfusion tracer on computer-based interpretation of myocardial perfusion images. For the image interpretation, an automated method was used based on image processing and artificial neural network techniques. A total of 1000 patients were studied, all referred to the Royal Brompton Hospital in London for myocardial perfusion scintigraphy over a period of 1 year. The patients were randomized to receive either thallium or one of the two technetium tracers, methoxyisobutylisonitrile or tetrofosmin. Artificial neural networks were trained with either mixed gender or gender-specific and mixed tracer or tracer-specific training sets of different sizes. The performance of the networks was assessed in separate test sets, with the interpretation of experienced physicians regarding the presence or absence of fixed or reversible defects in the images as the gold standard. The neural networks trained with large mixed gender training sets were as good as the networks trained with gender-specific data sets. In addition, the neural networks trained with large mixed tracer training sets were as good as or better than the networks trained with tracer-specific data sets. Our results indicate that the influence of patient gender and perfusion tracer are of minor importance for the computer-based interpretation of the myocardial perfusion images. The differences that occur can be compensated for by larger training sets. [source]