This paper describes a new method for measuring the end-to-end latency between sensing and actuation in a digital computing system. Compared to previous works, which generally measured the latency at 10-33-ms intervals or at discrete events separated by hundreds of ms, our new method measures the latency continuously at 1-ms resolution. This allows for the observation of variations in latency over sub 1-s periods, instead of relying upon averages of measurements. We have applied our method to two systems, the first using a camera for sensing and an LCD monitor for actuation, and the second using an orientation sensor for sensing and a motor for actuation. Our results show two interesting findings. First, a cyclical variation in latency can be seen based upon the relative rates of the sensor and actuator clocks and buffer times; for the components we tested, the variation was in the range of 15-50 Hz with a magnitude of 10-20 ms. Second, orientation sensor error can look like a variation in latency; for the sensor we tested, the variation was in the range of 0.5-1.0 Hz with a magnitude of 20-100 ms. Both of these findings have implications for robotics and virtual reality systems. In particular, it is possible that the variation in apparent latency caused by orientation sensor error may have some relation to simulator sickness.
A toxin producing phytoplankton-zooplankton model with inhibitory exponential substrate and time delay has been formulated and analyzed. Since the liberation of toxic substances by phytoplankton species is not an instantaneous process but is mediated by some time lag required for maturity of the species and the zooplankton mortality due to the toxic phytoplankton bloom occurs after some time laps of the bloom of toxic phytoplankton, we induced a discrete time delay to both of the consume response function and distribution of toxic substance term. Furthermore, based on the fact that the predation rate decreases at large toxicphytoplankton density, the system is modelled via a Tissiet type functional response. We study the dynamical behaviour and investigate the conditions to guarantee the coexistence of two species. Analytical methods and numerical simulations are used to obtain information about the qualitative behaviour of the models.
In this paper, we propose a novel technique on mining relationships in a sequential circuit to discover global constraints. In contrast to the traditional learning methods, our mining algorithm can find important relationships among several nodes efficiently. The nodes involved may often span several timeframes, thus improving the deductibility of the problem instance. Experimental results demonstrate that the application of these global constraints to SAT-based bounded sequential equivalence checking can achieve one to two orders of magnitude speedup. In addition, because it is orthogonal to the underlying SAT solver, it can help to enhance the efficacy of typical SAT based verification flows.
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