Efficient Parallel Statistical Model Checking of Biochemical Networks

We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, for example, CSL/PCTL model checking, are undermined by a huge computational demand which rule them out for most real case studies. Less demanding approaches, such as statistical model checking, estimate the likelihood that a property is satisfied by sampling executions out of the stochastic model. We propose a methodology for efficiently estimating the likelihood that a LTL property P holds of a stochastic model of a biochemical network. As with other statistical verification techniques, the methodology we propose uses a stochastic simulation algorithm for generating execution samples, however there are three key aspects that improve the efficiency: first, the sample generation is driven by on-the-fly verification of P which results in optimal overall simulation time. Second, the confidence interval estimation for the probability of P to hold is based on an efficient variant of the Wilson method which ensures a faster convergence. Third, the whole methodology is designed according to a parallel fashion and a prototype software tool has been implemented that performs the sampling/verification process in parallel over an HPC architecture

Validating the reliability of WCET estimates with MBPTA

Estimating the worst-case execution time (WCET) of tasks in a system is an important step in timing verification of critical real-time embedded systems. Measurement-Based Probabilistic Timing Analysis (MBPTA) is a novel and powerful method to compute WCET estimates based on measurements on the target platform. To provide reliable estimates, MBPTA needs to capture at analysis time the events with high impact on execution time. We propose a method to assess and increase the confidence that MBPTA captures the relevant events during analysis

A nonlinear estimator for reconstructing the angular velocity of a spacecraft without rate gyros

A scheme for estimating the angular velocity of a spacecraft without rate gyros is presented. It is based upon a nonlinear estimator whose inputs are measured inertial vectors and their calculated time derivatives relative to vehicle axes. It works for all spacecraft attitudes and requires no knowledge of attitude. It can use measurements from a variety of onboard sensors like Sun sensors, star trackers, or magnetometers, and in concert. It can also use look angle measurements from onboard tracking antennas for tracking and data relay satellites or global positioning system satellites. In this paper, it is applied to a Sun point scheme on the Hubble Space Telescope assuming all or most of its onboard rate gyros have failed. Simulation results are presented for verification