This work concerns the adaptation of sampling times for Linear Time Invariant (LTI) systems controlled by state feedback. Complementary to various works that guarantee stabilization independently of changes in the sampling rate, here we provide conditions to design stabilizing sequences of sampling instants. In order to reduce the number of these sampling instants, a dynamic scheduling algorithm optimizes, over a given sampling horizon, a sampling sequence depending on the system state value. Our proofs are inspired from switching system techniques combining Lyapunov functions and LMI optimization. In order to show the applicability of the technique, theoretical study is illustrated by an implementation in Matlab/TRUE TIME.

 Stabilization of continuous and discrete-time systems in the presence of network induced delays and model uncertainties is studied in this paper using the Model-Based Networked Control Systems (MB-NCS) framework. The use of a nominal model of the system to generate an estimate of the real state between measurement update intervals allows for significant reduction of traffic in the network. The work in this paper extends previous results in MB-NCS that dealt with continuous-time systems and small delays. In the current paper we are able to obtain necessary and sufficient conditions for stability for the case of large delays, that is, when the network delays are larger than the update intervals. Additionally, similar conditions are derived for discrete-time systems and for small and large delays

 A weighted digraph is balanced if, for each node, the sum of the weights of the edges outgoing from that node is equal to the sum of the weights of the edges incoming to that node. Weight-balanced digraphs play a key role in a number of applications, including cooperative control, distributed optimization, and distributed averaging problems. We address the weight-balance problem for a distributed system whose components (nodes) can exchange information via interconnection links (edges) that form an arbitrary, possibly directed, communication topology (digraph). We develop two iterative algorithms, a centralized one and a distributed one, both of which can be used to reach weight-balance, as long as the underlying communication topology forms a strongly connected digraph (or is a collection of strongly connected digraphs). The centralized algorithm is shown to reach weight-balance after a finite number of iterations (bounded by the numbers of nodes in the graph). The distributed algorithm operates by having each node adapt the weights on its outgoing edges and is shown to asymptotically lead to weight-balance. We also analyze the rate of convergence of the proposed distributed algorithm and obtain a (graph-dependent) worst-case bound on this rate of convergence. Finally, we provide examples to illustrate the operation, performance, and potential advantages of the proposed algorithms.

 In this article a survey on the different technologies in the area of Underwater Wireless Sensor Networks (UWSN) will be presented. The characteristics of these networks are different from those found in the terrestrial ones, while their architecture is vulnerable to various issues such as large propagation delays, mobility of floating sensor nodes, limited link capacity and multiple messages receptions due to reflections on the sea ground and sea surface. This article will present an overview of the underlying technologies in UWSN and will focus in presenting the most important research approaches towards UWSNs architecture, routing, MAC and localization protocols, energy consumption and security, while highlighting their most illustrative real-life applications.

 In this work detection of single and multiple leaks using an extended horizon analysis of pressure sensitivities is proposed. The method also includes other techniques, such as wavelet analysis, phase-quadrant demodulation code, and a weighting and voting system. This approach is tested in simulations for two different water distribution networks: Hanoi a network with high consumption, and Quebra, a larger network. Detection performance was measured by indicating the leaking node or one of the neighbors, and simulation results showed that in presence of two leaks, the efficiency is around 80%, detecting the leaking nodes or one of the neighbors around the leak, and 95% of efficiency to detect the leaking nodes, a neighbor node or the second order neighbors nodes, being these the neighbors of the neighbors; in presence of three leaks, the detection success is around 97% detecting the leaking nodes, the neighbor nodes or the second order neighbors nodes. Other simulations were also realized for single leak scenarios, showing our method better performance than a recently proposed algorithm.

 This paper deals with the decentralized closed loop control in a pure probabilistic framework. In this framework, a system is a controlled Markov chain whose transition probabilities depend on the actions of the agents. The agents are also described in a probabilistic way. The objective is to drive the system so that the joint state and agents actions are close to a set of given target probability distributions. The Kullback-Leibler divergence is used as a performance measure. The resulting algorithm uses dynamic programming interleaved with an iterative process that computes the behavior of each agent.