Monitoring Time Intervals

Run-time checking of timed properties requires to monitor events occurring within a specified time interval. In a distributed setting, working with intervals is complicated due to uncertainties about network delays and clock synchronization. Determining that an interval can be closed - i.e., that all events occurring within the interval have been observed - cannot be done without a delay. In this paper, we consider how an appropriate delay can be determined based on parameters of a monitoring setup, such as network delay, clock skew and clock rate. We then propose a generic scheme for monitoring time intervals, parameterized by the detection delay, and discuss the use of this monitoring scheme to check different timed specifications, including real-time temporal logics and rate calculations

Diagnostic Accuracy of Home Sleep Apnea Testing (HSAT) Based on Recording Duration

Introduction: Obstructive Sleep Apnea (OSA) is a chronic sleeping disorder with serious health consequences. Currently, standard diagnosis is through in-lab polysomnography; however, there has been a shift to greater use of Home Sleep Apnea Testing (HSAT) for patients with a high pre-test probability of having OSA. Objective: To investigate the minimum recording time needed during HSAT to accurately diagnose the presence and severity of OSA. Methods: A retrospective review was conducted of HSATs done from January-October 2017. Each study was divided into 1-, 2-,3-,4-,5-, 6-, and 7 hour intervals beginning at the recording start time. The respiratory event index (REI) was determined for each of these time intervals and then compared to the initial REI derived from the total monitoring time (REITMT) by a Fleiss’ κ test, a paired samples t-test, and concordance correlation coefficients (CCC). Results: Significant differences were found between the REITRT and the REI at 60 min (P \u3c 0.0001), 120 min (0.0002), 180 min (\u3c 0.0001) and 240 min (0.0002) with a lack of concordance, signifying these intervals are poor diagnostic correlates for the REITRT. REIs determined at 300, 360, and 420 min were not significantly different from the REITRT and had very significant CCCs, 0.979, 0.990, and 0.996, respectively. The Fleiss’ κ test showed almost perfect agreement between the REITRT and and the REI for 360 and 420 min. Discussion: The results suggest that at least 6 hours of monitoring time during HSAT is needed to accurrately diagnose and determine the severity of OSA

Effect of Time on Sensitivity and Specificity of Access Flow in Predicting Thrombosis

Dialysis access monitoring may help decrease thrombosis-related morbidity. We investigated the effect of time elapsed since an access flow measurement on test accuracy of a novel flow monitoring method called variable flow (VF) Doppler. A retrospective review was conducted in 36 patients with prosthetic grafts for vascular access using access thrombosis as the clinical endpoint. Receiver operator characteristic (ROC) curves and test sensitivity and specificity were determined for various follow-up time intervals. ROC analysis showed increasing test discrimination for shorter time intervals. Sensitivity and specificity for a commonly used surveillance threshold (600 ml/min) showed specificity that was little changed (88–93%) from follow-up time intervals of 15 days to 6 months. However, sensitivity was low (21%) at 6 months, increased to 50% at 2 months, 67% at 1 month, and 100% at 15 days (a single event). Low access blood flow using VF Doppler predicts near-term thrombosis. These data further imply that the discriminative value of access flow monitoring appears to be highly dependent on time from the flow measurement, improving with shorter time intervals from the measurement.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74923/1/j.1525-139X.2003.16107.x.pd

Technology Policy, Gender, and Cyberspace

Event based sampling occurs when the time instants are measured everytime the amplitude passes certain pre-defined levels. This is in contrast with classical signal processing where the amplitude is measured at regular time intervals. The signal processing problem is to separate the signal component from noise in both amplitude and time domains. Event based sampling occurs in a variety of applications. The purpose here is to explain the new types of signal processing problems that occur, and identify the need for processing in both the time and event domains. We focus on rotating axles, where amplitude disturbances are caused by vibrations and time disturbances from measurement equipment. As one application, we examine tire pressure monitoring in cars where suppression of time disturbance is of utmost importance

Estimates of avian collision with power lines and carcass disappearance across differing environments

Data on collisions of birds with high-voltage electric power lines are scarce and are often gathered without protocols for the correction of carcass disappearance. There is actually growing awareness that it is important to accomplish carcass removal trials in order to develop correction factors for producing adjusted estimates of mortality due to collisions. In this study, we provided for the first time raw counts and estimates of bird collisions across seven Italian areas that largely differ in their habitats. We also carried out carcass removal trials to compute the rate of carcass disappearance and produce better estimates of collision events and of optimal time intervals of carcass searches. Results of 1-year monitoring showed a general low frequency of birds collided with the power lines. Carcass removal trials showed effects of carcass size and season on the carcass disappearance, which varied largely among the study areas. In four areas, both small and large carcasses had more than 50% probability to be removed within 3–5 days from their distribution. Given the high variation among study areas, we suggest that estimates of carcass persistence and optimal time intervals should be conducted concurrently for each new study site

Selection of time instants and intervals with Support Vector Regression for multivariate functional data

When continuously monitoring processes over time, data is collected along a whole period, from which only certain time instants and certain time intervals may play a crucial role in the data analysis. We develop a method that addresses the problem of selecting a finite and small set of short intervals (or instants) able to capture the information needed to predict a response variable from multivariate functional data using Support Vector Regression (SVR). In addition to improving interpretability, storage requirements, and monitoring cost, feature selection can potentially reduce overfitting by mitigating data autocorrelation. We propose a continuous optimization algorithm to fit the SVR parameters and select intervals and instants. Our approach takes advantage of the functional nature of the data by formulating a new bilevel optimization problem that integrates selection of intervals and instants, tuning of some key SVR parameters and fitting the SVR. We illustrate the usefulness of our proposal in some benchmark data sets