Instrumentation used to measure residential magnetic fields and currents

The equipment used to measure magnetic fields and, electric currents in residences is described. The instrumentation consisted of current transformers, magnetic field probes and locally designed and, built signal conditioning modules. The data acquisition system was capable of unattended recording for extended time periods. The complete system was calibrated to verify its response to known physical inputs. (C) 2003 ISA-The Instrumentation Automation Society.

Characteristics of small-signal capacitances of silicon-on-sapphire MOSFETs

The measured inter-electrode capacitances of silicon-on-sapphire (SOS) MOSFETs are presented and compared with simulation results. It is shown that the variations of capacitances with DC bias differ from those of bulk MOSFETs due to change in body potential variation of the SOS device resulting from electron-hole pair generation through impact ionisation.

A matching pursuit-based signal complexity measure for the analysis of newborn EEG

This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity, which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed relative structural complexity measure is used in the analysis of newborn EEG. To do this, firstly, a time-frequency (TF) decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure).

Comparison of BR and QR Eigenvalue Algorithms for Power System Small Signal Stability Analysis

The BR algorithm is a novel and efficient method to find all eigenvalues of upper Hessenberg matrices and has never been applied to eigenanalysis for power system small signal stability. This paper analyzes differences between the BR and the QR algorithms with performance comparison in terms of CPU time based on stopping criteria and storage requirement. The BR algorithm utilizes accelerating strategies to improve its performance when computing eigenvalues of narrowly banded, nearly tridiagonal upper Hessenberg matrices. These strategies significantly reduce the computation time at a reasonable level of precision. Compared with the QR algorithm, the BR algorithm requires fewer iteration steps and less storage space without depriving of appropriate precision in solving eigenvalue problems of large-scale power systems. Numerical examples demonstrate the efficiency of the BR algorithm in pursuing eigenanalysis tasks of 39-, 68-, 115-, 300-, and 600-bus systems. Experiment results suggest that the BR algorithm is a more efficient algorithm for large-scale power system small signal stability eigenanalysis.

Analysis of small signal stability margins using genetic optimization

Power system small signal stability analysis aims to explore different small signal stability conditions and controls, namely: (1) exploring the power system security domains and boundaries in the space of power system parameters of interest, including load flow feasibility, saddle node and Hopf bifurcation ones; (2) finding the maximum and minimum damping conditions; and (3) determining control actions to provide and increase small signal stability. These problems are presented in this paper as different modifications of a general optimization to a minimum/maximum, depending on the initial guesses of variables and numerical methods used. In the considered problems, all the extreme points are of interest. Additionally, there are difficulties with finding the derivatives of the objective functions with respect to parameters. Numerical computations of derivatives in traditional optimization procedures are time consuming. In this paper, we propose a new black-box genetic optimization technique for comprehensive small signal stability analysis, which can effectively cope with highly nonlinear objective functions with multiple minima and maxima, and derivatives that can not be expressed analytically. The optimization result can then be used to provide such important information such as system optimal control decision making, assessment of the maximum network's transmission capacity, etc. (C) 1998 Elsevier Science S.A. All rights reserved.

Autoinhibition by an internal nuclear localization signal revealed by the crystal structure of mammalian importin alpha

Importin alpha is the nuclear import receptor that recognizes classical monopartite and bipartite nuclear localization signals (NLSs). The structure of mouse importin alpha has been determined at 2.5 Angstrom resolution. The structure shows a large C-terminal domain containing armadillo repeats, and a less structured N-terminal importin beta-binding domain containing an internal NLS bound to the NLS-binding site. The structure explains the regulatory switch between the cytoplasmic, high-affinity form, and the nuclear, low-affinity form for NLS binding of the nuclear import receptor predicted by the current models of nuclear import. Importin beta conceivably converts the low- to high-affinity form by binding to a site overlapping the autoinhibitory sequence. The structure also has implications for understanding NLS recognition, and the structures of armadillo and HEAT repeats.

RWMODEL II: Computer simulation of the Rescorla-Wagner model of Pavlovian conditioning

RWMODEL II simulates the Rescorla-Wagner model of Pavlovian conditioning. It is written in Delphi and runs under Windows 3.1 and Windows 95. The program was designed for novice and expert users and can be employed in teaching, as well as in research. It is user friendly and requires a minimal level of computer literacy but is sufficiently flexible to permit a wide range of simulations. It allows the display of empirical data, against which predictions from the model can be validated.

Is affective learning really resistant to extinction? Stimulus ratings and affective priming give different answers

Affective learning, the learning of likes and dislikes, is proposed to differ from signal learning, the learning of relationships between events. However, affective learning research varies in the methodology used, and in addition, researchers concerned primarily with affective learning tend to use different paradigms from those concerned with signal learning. The current research used an affective priming task in addition to verbal ratings to assess changes in the valence of neutral geometric shapes in an aversive differential conditioning procedure. After acquisition, affective learning was present as indexed by ratings and affective priming, whereas after extinction, affective learning remained significant only in the ratings. This study suggests that different measures of affective learning may be differentially sensitive to valence, which has implications for studies that employ verbal ratings as the sole measure of affective learning. Moreover, there is no evidence from the current study that affective learning differs from signal learning.

Startle-potentiation during nonaversive human conditioning

Fear-potentiated startle is a well-established measure of emotional learning in nonhuman animals. In humans, startle potentiation in anticipation of an aversive unconditional stimulus (US) has been interpreted as reflecting the same emotional process. This interpretation was supported by previous failures to fmd startle potentiation in anticipation of nonaversive USs, reactiontime tasks. The present research questions these results. Experiment 1 found startle-potentiation in anticipation of an aversive US, which resulted in increased dislike of the conditional stimulus (CS), and in anticipation of a nonaversive US, which did not affect CS valence. Experiment 2 replicated the latter finding, indicating that provision of performance feedback enhanced the salience of the reaction time task USs and thus anticipatory startle potentiation. The present results pose problems for the interpretation of fmdings of potentiated startle in human-aversive conditioning as reflecting emotion. Rather, startle potentiation during aversive and non-aversive conditioning may reflect the attentional processes known to occur during human-associative learning.