GCM: a grid extension to Fractal for autonomous distributed components

This article presents an extension of the Fractal component model targeted at programming applications to be run on computing grids: the grid component model (GCM). First, to address the problem of deployment of components on the grid, deployment strategies have been defined. Then, as grid applications often result from the composition of a lot of parallel (sometimes identical) components, composition mechanisms to support collective communications on a set of components are introduced. Finally, because of the constantly evolving environment and requirements for grid applications, the GCM defines a set of features intended to support component autonomicity. All these aspects are developed in this paper with the challenging objective to ease the programming of grid applications, while allowing GCM components to also be the unit of deployment and management.     

LC ladder used as broadband prototype for distributed components

Line length is an alternative variable parameter in distributed-component design when there are constraints on choice of impedance. By means of an analogy with the synthetised LC ladder formulas, simple expressions have been found which permit the use of either variable. Application to the design of dielectric-layer filters in waveguide 16 is discussed.     

Consistency in models for distributed learning under communication constraints

Motivated by sensor networks and other distributed settings, several models for distributed learning are presented. The models differ from classical works in statistical pattern recognition by allocating observations of an independent and identically distributed (i.i.d.) sampling process among members of a network of simple learning agents. The agents are limited in their ability to communicate to a central fusion center and thus, the amount of information available for use in classification or regression is constrained. For several basic communication models in both the binary classification and regression frameworks, we question the existence of agent decision rules and fusion rules that result in a universally consistent ensemble; the answers to this question present new issues to consider with regard to universal consistency. This paper addresses the issue of whether or not the guarantees provided by Stone's theorem in centralized environments hold in distributed settings.     

CLIF, a framework based on Fractal for flexible, distributed load testing

The context of this work is performance evaluation of IT systems based on load testing. It typically consists in generating a flow of requests on a system under test, and to measure response times, request throughput, or computing resource usage. A quick overview of available load testing platforms shows that there exist hundreds of such platforms, including in the open source domain. However, many testers still tend to develop their own ad hoc load testing tooling. Why? This paper starts by looking for possible answers to this question, in order to introduce the CLIF load injection framework, which intends not to be yet another load testing platform. Based on the Fractal component model, the CLIF open source project aims at addressing key issues such as flexibility, adaptation, and scalability. We give here details about CLIF’s architecture and associated tools as well as some feedback from a bunch of practical utilizations.     

Efficient passive circuit models for distributed networks withfrequency-dependent parameters

This paper presents an efficient method for the analysis of multiconductor transmission lines with frequency-dependent parameters. The proposed technique generates positive-real representations for the frequency dependency of transmission line parameters as well as closed-form expressions based on exponential Pade approximants. The new model is suitable for inclusion in general purpose circuit simulators and overcomes the difficulty of mixed frequency/time simulation encountered during transient analysis. In addition, the proposed model can be easily incorporated with the recently developed passive model-reduction techniques. Numerical examples are presented to demonstrate the validity and efficiency of the proposed method     

Two-DOF magnetic orientation sensor using distributed multipole models for spherical wheel motor

This paper presents a new method for measuring a two degree-of-freedom (DOF) orientation of a permanent magnet (PM) based system using magnetic field measurements. The method exploits distributed multipole (DMP) modeling method to accurately predict a magnetic field, and provides a rational basis to inversely solve for the orientation of the PM from measured data. The PM-based magnetic sensor along with the ability to characterize the magnetic field in real-time offers advantages in sensing and control such as contact-free measurements eliminating frictional wears commonly encountered in existing designs with a combination of single-axis encoders, and high-speed sampling rate thus offering a higher bandwidth than methods based on imaging sensors. This paper demonstrates the efficient method capable of measuring the orientation of the PM by implementing it on a spherical wheel motor (SWM), where the two-DOF orientation is measured. Sensor performance has been studied both analytically and experimentally to validate the DMP-based sensor model. The results can offer valuable insights for optimizing contact-free sensor designs.     

Performability of integrated software-hardware components ofreal-time parallel and distributed systems

A method for analyzing performability of the integrated software-hardware components of parallel and distributed systems is presented. The technique uses generalized stochastic Petri nets at the system level (analysis of integrated software-hardware systems). The intractable problem of evaluating a parallel software environment consisting of interacting fault-tolerant parallel tasks is addressed. This is accomplished using a decomposition technique at the task-graph level, where the task graph is decomposed into segments. Recovery blocks are effectively modeled in the interacting parallel modules as well as their supporting hardware. This method greatly facilitates the analysis of performability at the system level. However, the integrated performability model increases the size of the Markov generator matrix. This issue is addressed, and a performability decomposition technique at the task-graph level presented is illustrated by a simple example of a radar command center     

Human perception-based distributed architecture for scalable video conferencing services: theoretical models and performance

This research work proposes a human perception-based distributed architecture for the multiparty video conferencing services. The new architecture can effectively reduce the unnecessary traffic of the multilayer video streams on the overlay network. Rich theoretical models of the three different architectures: the proposed perception-based distributed architecture, the conventional centralized architecture, and perception-based centralized architecture have been constructed by using queuing theory to reflect the traffic generated, transmitted, and processed by the three architectures. The performance has been considered in different aspects from the total waiting time to the required service rates. Together, the modeling tools, the analysis, and the numerical results help to answer the common concern about advantages and disadvantages of the centralized and distributed architectures. Overall, the proposed human perception-based distributed architecture can maintain a smaller total waiting time with a much smaller requirement of service rate in comparison with the conventional centralized architecture and perception-based centralized architecture.     

Markov chain reduction and analysis of GSPN models for task allocation in distributed systems

In this paper, we present a Markov chain (MC) reduction technique for analysis of a certain class of MCs. It requires only O(t) steps to analyze an MC having t states by this technique, as compared to O(t³) steps required by the usual matrix inversion method. We also present a generalized stochastic Petri net (GSPN) model for task allocation in distributed computer systems, where execution and inter-module communication costs are treated as random variables with exponential probability distribution. We show that the MC of this task system falls into a class to which the reduction technique is applicable. As an illustration of the reduction technique, we use a GSPN model of the task system to find the mean completion time of the system.     

Age-dependent models for evaluating risks and costs of surveillanceand maintenance of components

To determine in a quantitative way how surveillance and maintenance activities are reflected in component reliability, some age-dependent models to determine risks and associated economic costs are presented which explicitly consider how activities affect the component age as a function of the maintenance effectiveness. The models are based on reliability parameters such as demand-failure probability, standby failure rate, aging rate, and degradation rate. Also introduced are the concept of `characteristic function of maintenance' that states the relationship between maintenance effectiveness, its period, and the age setback. Several sensitivity analyses are performed on the reliability parameters and the maintenance effectiveness that allow establishment of important differences among activities with regard to their risks and costs. These models are: (1) important in maintenance improvement, particularly within the reliability centered maintenance (RCM) methodology; (2) useful in finding a degradation vs maintenance effectiveness relationship. They are used in assessing maintenance effectiveness within a program based on RCM at Cofrentes Nuclear Power Plant (Spain), where several policies of integrating surveillance and maintenance activities are analyzed     

Coupled inductance and reluctance models of magnetic components

Different models for linear transformers are discussed relative to their use in circuit simulation. These models include: the reluctance model, the permeance model, the inductance matrix model, the T-model and the reduced T-model. Equations relating the parameter values of the models are derived for the two- and three-winding transformers. A method for direct measurement of the inductance matrix model parameters is described. Guidance is provided for deriving model parameters and using the models in circuit simulators. A model of an ideal transformer using controlled current and voltage sources useful for the T-model is derived. Several examples are discussed     

Reliability bounds for some static system models using lifetime data on their components

It has become increasingly difficult to obtain a sampling distribution of the reliability function of a system. Therefore, reliability bounds have been studied to facilitate the analysis of reliability characteristics. The bounds for the reliability of the system depend on the mean and variance of the lifetimes recorded on the system. However, in the early stages of design, the lifetime data on a system may either be costly or non-existent. Thus, by using lifetime data on the components of the system, the present study deals with the analysis of reliability bounds for some static system models.     

Empirical model generation techniques for planar microwave components using electromagnetic linear regression models

Accurate and efficient empirical model generation techniques of microwave devices, for a large range of geometric and material parameters opportunely chosen, are presented. The empirical models are based on multiple linear regression approach, which compensates the error between an initial inaccurate empirical model and an electromagnetic (EM) full-wave solver (or measurement data). The aim of these techniques is to generate accurate empirical models, which are computationally very efficient with respect to any EM technique. These simple models could be integrated in a toolbox of any commercially available computed-aided design tools for RF/microwave circuits. Comparisons with artificial neural networks and linear-regression-based models are listed and discussed for the dispersion of a microstrip transmission line propagating the quasi-TEM mode and a microwave tunable phase shifter propagating the even mode.     

常规雷达飞机目标旋转部件调制特性的仿真模型与分析

主要推导固定翼飞机回波理论参数模型和旋转桨飞机回波理论参数模型并进行仿真,对两者进行比较,提取目标旋转部件的调制周期特征,有利于目标的识别与分类。     

Fractal modeling techniques for spatial data

The paper presents convolutional linear data models for the processing of one-dimensional (1D) and two-dimensional (2D) spatial data. The models assume that the measured data is the superposition of a stochastic innovation process and a deterministic system function. The innovation process is described by a fractal scaling noise, which has a power spectral density proportional to some power (-β) of the frequency. The system function is assumed to be symmetric and is constructed using autoregressive (AR) filtering procedures. Iterative deconvolution procedures are developed to recover the fractal innovation from the data. For computational convenience, these procedures assume a spectrally white (β=0) innovation, but modify the data prior to inversion by prewhitening the a priori assumed fractal innovation. The filter coefficients recovered by inverting the modified data are then applied to the original data to recover the fractal innovation. The ability of the deconvolution procedures to recover the fractal innovation is demonstrated using 1D and 2D synthetic data sets. As a practical example, the 2D deconvolution technique is applied to an aeromagnetic map from northeastern Ontario, Canada, and is shown to be effective in enhancing magnetic field anomalies     

Low-complexity estimators for distributed sources

In antenna array applications, the propagation environment is often more complicated than the ordinarily assumed model of plane wavefronts. Here, a low-complexity algorithm is suggested for estimating both the DOA and the spread angle of a source subject to local scattering, using a uniform linear array. The parameters are calculated from the estimates obtained using a standard algorithm such as root-MUSIC to fit a two-ray model to the data. The algorithm is shown to give consistent estimates, and the statistical performance is studied analytically and through simulations