This paper focuses on the passive control problem for Markov jump systems with piecewise-constant transition rates, random nonlinearities and random communication delays. An observer is used to obtain the system state, and communication delay with probabilistic distribution is considered. The probabilistic properties of the nonlinear perturbations and communication delays are described by two mutually independent Bernoulli sequences. Based on average dwell time method and Lyapunov stability theory, a sufficient stochastic passivity condition is presented. By singular value decomposition technique the gain matrices can be computed via strict linear matrix inequalities, and the desired controller guarantees the stochastic passivity of the closed-loop augmented system. The effectiveness of the method is illustrated by a numerical example.

Video supervision equipment, which is readily available in most cities, can record the processes of urban floods in video form. Ubiquitous reference objects, which often appear in videos, can be used to indicate urban waterlogging depths. This makes video images a valuable data source for obtaining waterlogging depths. However, the urban waterlogging information contained in video images has not been effectively mined and utilized. In this paper, we present a method to automatically estimate urban waterlogging depths from video images based on ubiquitous reference objects. First, reference objects from video images are detected during the flooding and non-flooding periods using an object detection model with a convolutional neural network (CNN). Then, waterlogging depths are estimated using the height differences between the detected reference objects in these two periods. A case study is used to evaluate the proposed method. The results show that our proposed method could effectively mine and utilize urban waterlogging depth information from video images. This method has the advantages of low economic cost, acceptable accuracy, high spatiotemporal resolution, and wide coverage. It is feasible to promote this proposed method within cities to monitor urban floods.

Existing node deployment algorithms for underwater sensor networks are nearly unable to improve the network coverage rate under the premise of ensuring the full network connectivity and do not optimize the communication and move energy consumption during the deployment. Hence, a node deployment algorithm based on connected dominating set (CDS) is proposed. After randomly sowing the nodes in 3D monitoring underwater space, disconnected nodes move to the sink node until the network achieves full connectivity. The sink node then performs centralized optimization to determine the CDS and adjusts the locations of dominated nodes. Simulation results show that the proposed algorithm can achieve a high coverage rate while ensuring full connectivity and decreases the communication and movement energy consumption during deployment.