Pupillary responses to sinusoidal light stimuli were measured over a range of light levels and frequencies. The phase lag and equivalent time delay of these responses were reduced in an approximately log-linear fashion with increasing mean light level (slope = -60 ms/log unit). The magnitude of this level dependence is reduced at higher frequencies, and at higher light levels. This nonlinear level dependent signal flow (LDSF) effect is shown to be essentially independent of target distance (accommodative stimulus) which influences pupil size, and of pupil size itself. Thus most of the level dependence probably resides in the afferent path of the light-pupil reflex arc, before the accommodation signal joins the light signal in the Edinger-West-phal nucleus. A systems model is presented to the LDSF effect described here and in the companion papers (Myers and Stark 1993a, b). When parameters of the model are adjusted to fit pupillary responses to transient light stimuli over a range of light levels, the model simulates reduced phase lag in response to increased mean light level, and the reduction in this LDSF effect with increased mean light level or increasing stimulus frequency without further changes in parameters. This latter reduction explains the relatively small level dependence seen in latency data (-34 ms/log unit). These data will be shown (Myers and Stark 1990b) to be commensurate with reduction in pupil cycle time (increased frequency of oscillation) observed in high gain oscillation experiments as mean brightness increases.(ABSTRACT TRUNCATED AT 250 WORDS)
The complexity of tracking perturbations in discrete event dynamic systems (DEDS) depends on the systems' perturbation propagation mechanism and on the length of the event trace. Existing perturbation propagation algorithms assume that all unperturbed event times are observed and that all perturbed times are required. This paper concerns a complementary approach, termed perturbation tracking (PT), that accurately tracks perturbations in systems for which only a subset of event times are known. We apply P T to a class of partially-observed, timed Petri nets and show that for accurate tracking it is necessary and sufficient to know the token holding times between observations. We conclude with an example, motivated by a practical software monitoring problem, that illustrates how this information can be derived from structural and event trace analysis. Not surprisingly, the perturbation propagation rules of our PT algorithm are closely related to the existing algorithms when all event timings are observed.
Execution monitoring plays a central role in most software development tools for parallel and distributed computer systems. However, such monitoring may induce delays that corrupt event timing. If this corruption can be quantified it may be possible to determine the intrusion-free behavior. In this paper we describe an algorithm that, given a safe timed Petri net model of the monitored software, can determine the uncorrupted timestamp values, i.e., those that would have been observed had the delays not been present. Monitoring conditions suficient to ensure correct operation of the algorithm, and examples illustrating the algorithm's applicability to message-passing systems are also presented. This work is part of a larger effort aimed at identifying cost effective software alternatives to custom hardware monitoring.
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