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This article considers the problem of monitoring a normally distributed process variable when a special cause may produce a time-varying linear drift in the mean. The design and application of a generalized likelihood ratio (GLR) control chart for drift detection are evaluated. The GLR drift chart does not require specification of any tuning parameters by the practitioner and has the advantage that, at the time of the signal, estimates of both the change point and the drift size are immediately available. An equation to accurately approximate the control limit is provided. The performance of the GLR drift chart is compared with that of other control charts such as a standard cumulative sum chart and a cumulative score chart designed for drift detection. We also compare the GLR chart designed for drift detection with the GLR chart designed for sustained shift detection because both of them require only a control limit to be specified. In terms of the expected time for detection and in terms of the bias and mean squared error of the change-point estimators, the GLR drift chart has better performance for a wide range of drift rates relative to the GLR shift chart when the out-of-control process is truly a linear drift.

In recent research on 3D underwater wireless sensor network (UWSN), magnetic induction communication is a promising candidate, thanks to several unique features, such as small transmission delay, constant channel behavior, and adequate long communication range. However, designing a routing protocol that prolongs the network lifetime and reduces the transmission delay has been still a challenge for a 3D UWSN. In this paper, we propose an efficient routing protocol based on reinforcement learning, in particular, the Q-learning that aims to investigate the resource management in the hierarchical networks. Through defining the single hopping bonus metrics of distance and energy, we deduce the updating formula of the routing algorithm and derive the relationship between energy priority and distance priority. In addition, we set up a regulatory factor to adjust the proportion between energy saving and low delay, and thus, it can meet different needs. The simulation results show that the proposed routing approach outperforms the conventional protocol in extending the network lifetime and reducing the transmission delay.INDEX TERMS Underwater wireless sensor network, magnetic induction, routing protocol, reinforcement learning.

Magnetic induction (MI) communication is a promising technology for next-generation low-power underwater wireless sensor networks (UWSNs). Clustering algorithm design becomes an important and challenging issue in today's MI-based UWSNs. In contrast to the conventional approaches which suffer from continuous movement of ocean current and traffic loads in different areas of the network, we consider a clustering algorithm based on the Voronoi diagram and node density distribution to improve the energy efficiency and to prolong the network lifetime. In particular, we propose a jellyfish breathing process for cluster head selection and an automatic adjustment algorithm for sensor nodes. The simulation results show that the proposed clustering algorithm achieves a high network capacity rate and a good equalization for the remaining energy.INDEX TERMS Underwater sensor network, magnetic induction communication, clustering algorithm, Poisson point distribution, Voronoi diagram, jellyfish breathing process.

Accurate phase unwrapping (PU) is a precondition and key for using synthetic aperture radar interferometry (InSAR) technology to successfully invert topography and monitor surface deformations. However, most interferograms are seriously polluted by noise in the low-quality regions, which poses difficulties for PU. Therefore, using the strategy of leveling network adjustment, this paper proposes an improved PU method based on hierarchical networking and constrained adjustment. This method not only limits the phase error transfer of low-quality points, but also takes the PU results of high-quality points as control points and uses the network adjustment method with constraints to unwrap low-quality points, which effectively inhibits the influence of noise and improves the accuracy of unwrapping. Regardless of the unwrapping method used for high-quality points, the unwrapping accuracy of low-quality points can always be improved. Compared with other traditional two-dimensional phase unwrapping workflows, this method can more accurately recover the phase of low-coherence regions only through the interferogram. A simulation experiment showed that the local noise of the interferogram was effectively inhibited, and the PU accuracy of the low-quality regions was improved by 16–46% compared with different traditional methods. For a real-data experiment of mining area with low coherence, the PU result of our proposed method had fewer residues and lower phase standard deviation than traditional methods, further indicating the practicability and robustness of the proposed method. The work in this paper has considerable practical significance for recovering the decoherence phase with serious local noise such as mining centers and groundwater subsidence centers.