Measurement-based network monitoring and inference: scalability andmissing information

Using measurements collected at network monitors to infer network conditions is a promising approach for network-centric monitoring. In this context, an important question arises: given the number and locations of network monitors, how much network management resources (e.g., the number of measurements) are needed to obtain an accurate estimate of network states? We define the scalability of measurement-based network monitoring as the growth rate of the number of measurements required for accurate network monitoring/inference with respect to the size of a network. We develop a framework for investigating the scalability in the context of multicast inference with the monitors at the edges of a network. In such a framework, network monitoring/inference can be formulated as probability density estimation of network states. The growth rate is characterized through the sample complexity, which is the number of measurements needed to accurately estimate the density. The missing data framework is introduced to estimate the growth rate, where the missing data reflect unavailable measurements at the unobservable nodes without resident monitors, and the underlying nodal packet losses. We show that when the missing information is mainly due to the number of unobservable nodes, the number of measurements needed grows linearly with the size of the network, and the measurement-based inference approach is, thus, scalable. When the missing information is mainly due to the underlying nodal packet losses, the number of measurements needed grows faster than linear with the size of the network, and the measurement-based inference approach is, thus, not scalable. Our results provide guidelines for accessing feasibility of the measurement-based inference approach, and the number of probes required. We give numerical examples to illustrate some of our results     

Assessment of underground cable ratings based on distributed temperature sensing

Numerous power utilities have installed advanced distributed temperature sensing (DTS) systems to measure the temperature of underground cables. This paper presents a general procedure for real-time assessment of underground cable ratings based on DTS. After the identification and location of hot spots on a cable, the cable installation data, load, and DTS-recorded temperature over a period of time are used to estimate soil thermal parameters and assess cable ratings under normal and overloading conditions. In order to accurately estimate soil thermal parameters, a combined technique of the finite-element method and gradient-based optimization is used. The technique was validated in the laboratory and applied to a rating assessment of a 230-kV/500-MVA cable circuit. The assessed continuous and emergency ratings are helpful in improving the operation of this circuit.     

Exploratory analysis of brain connectivity with ICA.

Covariance-based methods of exploration of functional connectivity of the brain from functional magnetic resonance imaging (fMRI) experiments, such as principal component analysis (PCA) and structural equation modeling (SEM), require a priori knowledge such as an anatomical model to infer functional connectivity. In this research, a hybrid method, combining independent component analysis (ICA) and SEM, which is capable of deriving functional connectivity in an exploratory manner without the need of a prior model is introduced. The spatial ICA (SICA) derives independent neural systems or sources involved in task-related brain activation, while an automated method based on the SEM finds the structure of the connectivity among the elements in independent neural systems. Unlike second-order approaches used in earlier studies, the task-related neural systems derived from the ICA provide brain connectivity in the complete statistical sense. The use and efficacy of this approach is illustrated on two fMRI datasets obtained from a visual task and a language reading task.     

A neural network control system with parallel adaptive enhancementsapplicable to nonlinear servomechanisms

In this paper, we present a technique for using an additional parallel neural network to provide adaptive enhancements to a basic fixed neural network-based nonlinear control system. This proposed parallel adaptive neural network control system is applicable to nonlinear dynamical systems of the type commonly encountered in many practical position control servomechanisms. Properties of the controller are discussed, and it is shown that if Gaussian radial basis function networks are used for the additional parallel neural network, uniformly stable adaptation is assured and the approximation error converges to zero asymptotically. In the paper, the effectiveness of the proposed parallel adaptive neural network control system is demonstrated in real-time implementation experiments for position control in a servomechanism with asymmetrical loading and changes in the load     

Enhancement of oxide break-up by implantation of fluorine inpoly-Si emitter contacted p+-n shallow junctionformation

In this study, it is demonstrated that the incorporation of fluorine can enhance poly-Si/Si interfacial oxide break-up in the poly-Si emitter contacted p⁺-n shallow junction formation. The annealing temperature for breaking up the poly-Si/Si interfacial oxide has been found to be as low as 900°C. As a result, the junction depth of the BF₂-implanted device is much larger than that of the boron-implanted device     

Surface leakage currents in SiN/sub x/ passivated AlGaN/GaN HFETs

A novel guarded surface leakage test structure is used to isolate the surface and bulk leakage contributions to gate current in AlGaN/GaN HFETs. Passivation with various recipes of SiN/sub x/ always resulted in the commonly observed increase in gate leakage, which was found to be dominated by bulk leakage through the AlGaN. However, high temperature deposited SiN/sub x/ recipes gave a 1-2 orders reduction in surface leakage, whereas low temperature deposition gave an increase. Gate lag measurements were found to correlate closely with the surface leakage component, giving direct evidence that the key device problem of current slump is associated with current flow at the AlGaN surface.     

Study of miscibility enhancement in poly(styrene‐co‐4‐vinylphenol)/poly(ethylene oxide) blends by the spin labelling method

The electron spin resonance (ESR) spectra of end‐group spin labelled poly(ethylene oxide) (SLPEO) using 2,2,6,6‐tetramethyl‐piperdine‐1‐oxyl nitroxide and its blends with poly(styrene‐co‐4‐vinylphenol) (STVPhs) of different hydroxyl contents were recorded over a wide temperature range. For a blend of SLPEO and pure polystyrene (PS), the ESR spectrum was composed of a single motion component, indicating that PS was immiscible with PEO. For blends composed of SLPEO and different‐hydroxyl‐content STVPhs, two spectral components with different motion rates were observed over a certain temperature range. The difference between the motion rates should be attributed to micro‐heterogeneity in the blends, with the faster rate corresponding to a nitroxide radical motion trapped in the PEO‐rich domain and the slower rate corresponding to a nitroxide radical motion trapped in the STVPh‐rich domain. Variations in the values of a number of the ESR parameters (T_a, T_d and T_50G) and the apparent activation energy (E_a) with hydroxyl content in the blends indicated that the miscibility of the blends increased with increasing hydrogen‐bonding density due to specific interactions between the hydroxyl groups in STVPh and the ether oxygens in PEO. Copyright © 2004 Society of Chemical Industry