Creating cyber-physical systems (CPSs) based on wireless sensor and actuator networks (WSANs) has great potential to improve the performance of Smart Grid. In addition, IEEE802.15.4 has widely been regarded as an appropriate standard for WSANs, due to some striking and unique features. WSANs require provisioning strict quality of service (QoS) due to noisy harsh environments in Smart Grid applications. Although analytical models have been studied in the literature, they have not provided a full-fledged model for Smart Grid. In this paper, we have added a MAC-level buffer, and a novel Markov chain model has been also proposed. By comparison with previous studies, retransmission confines, acknowledgment, packet length variation, saturated traffic, and degenerate distribution of packet generation are accounted for. The algorithm has been experimentally implemented and appraised on a platform with self-designed WSAN. The analytical model predicts well our exhaustive experiments. Further, Monte Carlo simulations validate mathematical results.

A new technique for improving the diffusion barrier properties of thin, thermallyevaporated nickel, chromium and nichrome films on silicon is described. In this technique, known as “Rapid Thermal Annealing” (RTA), profound differences in the diffusion barrier properties of the films annealed in ammonia ambient at 550-750°C, in comparison to films annealed only in vacuum, were observed. The films annealed in ammonia retained their integrity while the films annealed in vacuum showed diffusion of the silicon into the metal overlayer throughout the entire thickness of the metal in some cases. The film sheet resistance increase for the latter was consistent with the formation of the metal silicide. The possibility of extending this technique to electroplated films used in integrated and hybrid device fabrication is being studied.

<p>We evaluate our method on two public cardiac MRI segmentation dataset. First the dataset from the Automated Cardiac Diagnosis Challenge (ACDC 2017) is employed. The second dataset used in this article is the M&M challenge dataset </p>

<p>We evaluate our method on two public cardiac MRI segmentation dataset. First the dataset from the Automated Cardiac Diagnosis Challenge (ACDC 2017) is employed. The second dataset used in this article is the M&M challenge dataset </p>