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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We propose a novel locally adaptive learning estimator for enhancing the inter-and intra-discriminative capabilities of Deep Neural Networks, which can be used as improved loss layer for semantic image segmentation tasks. Most loss layers compute pixel-wise cost between feature maps and ground truths, ignoring spatial layouts and interactions between neighboring pixels with same object category, and thus networks cannot be effectively sensitive to intra-class connections. Stride by stride, our method firstly conducts adaptive pooling filter operating over predicted feature maps, aiming to merge predicted distributions over a small group of neighboring pixels with same category, and then it computes cost between the merged distribution vector and their category label. Such design can make groups of neighboring predictions from same category involved into estimations on predicting correctness with respect to their category, and hence train networks to be more sensitive to regional connections between adjacent pixels based on their categories. In the experiments on Pascal VOC 2012 segmentation datasets, the consistently improved results show that our proposed approach achieves better segmentation masks against previous counterparts.

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In this paper, a reaction-diffusion SIR epidemic model is proposed. It takes into account the individuals mobility, the time periodicity of the infection rate and recovery rate, and the general nonlinear incidence function, which contains a number of classical incidence functions. In our model, due to the introduction of the general nonlinear incidence function, the boundedness of the infected individuals can not be obtained, so we study the existence and nonexistence of periodic traveling wave solutions of original system with the aid of auxiliary system. The basic reproduction number and the critical wave speed are given. We obtained the existence and uniqueness of periodic traveling waves for each wave speed using the Schauder’s fixed points theorem when . The nonexistence of periodic traveling waves for two cases (i) and (ii) and are also obtained. These results generalize and improve the existing conclusions. Finally, the numerical experiments support the theoretical results. The differences of traveling wave solution between periodic system and general aperiodic coefficient system are analyzed by numerical simulations.