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With the booming development of marine exploration technology, new studies such as the oceanix city, smart coastal city, and underwater smart cities have been proposed, and the Internet of Underwater Things (IoUT) has received a lot of attention. Data collection is an important application of the IoUT. The common method is to collect data by traversing the network using underwater intelligent devices, such as Autonomous Underwater Vehicles (AUVs). However, traditional data collection methods focus more on issues, such as path planning or the task assignment of AUVs. It is commonly known that the MAC protocol plays a crucial role in data transmission, which is designed to solve the competition issue for shared channels. However, the research on MAC is very challenging owing to the characteristics of hydroacoustic communication, e.g., the low bandwidth, high error rate, and long transmission latency. Hence, this paper proposes a MAC protocol based on an Interference Cancellation Graph (ICG-MAC) for AUV-assisted IoUT. It ensures that AUVs can join the network for data transmission immediately after arriving at the target area and they do not interfere with the normal work of other sensor nodes. Firstly, the target area to be reached by an AUV for data collection is defined according to the node degree and residual energy; then the interference model between neighboring nodes is analyzed and an Interference Cancellation Graphx is established, based on which the time slots are allocated for sensor nodes; and finally, the AUV moves to the target area for conflict-free data collection. The simulation results show that the proposed algorithm outperforms the comparison algorithms in terms of the network throughput and energy consumption. With the assistance of an AUV, better network connectivity and higher network traffic can be obtained. [ABSTRACT FROM AUTHOR]

In modern semiconductor manufacturing, one type of measured particle count data contains excess zeros, and the ratio of zeros in the measurements is usually larger than 50%. This type of particle count sample data cannot be well modeled by popular defect models such as Poisson, zero-inflated Poisson, generalized zero-inflated Poisson, and Neyman and Gamma-Poisson models. In this paper, a threshold-Poisson model was proposed to describe the particles with excess zero counts, and the method for parameter estimation was developed. Via comparison with those popular models by using 15 measured samples, it showed that the measurements are better modeled by the threshold-Poisson model. A control chart called threshold-c control chart was proposed and the control limits were derived. A reasonable minimum sample size for constructing control chart was also discussed based on simulations. [ABSTRACT FROM AUTHOR]

Traditional variables control charts assume that process data is independently identically normally distributed (IIND). Some real-world processes, however, do not obey this convenient assumption. The semiconductor industry is very familiar with non-IIND distributions. The batch processes in the semiconductor manufacturing involve nested variation sources. The variation sources are between-batch variation, the within-batch (or between-wafers) variation in each batch, and the within-wafer variation in each wafer. First, it is proved that the application of traditional mean control chart in the batch process would result in too many false alarms. In addition, some kinds of control charts proposed in previous studies either cannot be used to monitor all the three variation sources or contain too many control charts. In this paper, we propose a new control chart named as two-level nested control chart, composing of four control charts for synthetically monitoring the batch process, which can monitor all the three variation sources. Finally, it was proved by practical application in the process of phosphorus diffusion that this new control chart can be used to monitor all the three variation sources of batch processes. [ABSTRACT FROM AUTHOR]