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In this paper, the robust fault detection problem is investigated for a class of discrete-time networked systems with unknown input and multiple state delays. A novel measurement model is utilized to represent both the random measurement delays and the stochastic data missing phenomenon, which are typically resulted from the limited capacity of the communication networks. Network status is assumed to vary in a Markovian fashion and its transition probability matrix is uncertain but reside in a known convex set of a polytopic type. The main purpose of this paper is to design a robust fault detection filter such that, for all unknown inputs, possible parameter uncertainties as well as incomplete measurements, the error between residual and fault is made as small as possible. By casting the addressed robust fault detection problem into an auxiliary robust H∞ filtering problem of a certain Markovian jumping system, a sufficient condition for the existence of the desired robust fault detection filter is established in terms of linear matrix inequalities. A numerical example is provided to illustrate the effectiveness and applicability of the proposed technique.

Abstract-In this paper, the distributed filtering problem is investigated for a class of discrete time-varying systems with an event-based communication mechanism. Each intelligent sensor node transmits the data to its neighbors only when the local innovation violates a predetermined Sendon-Delta (SoD) data transmission condition. The aim of the proposed problem is to construct a distributed filter for each sensor node subject to sporadic communications over wireless networks. In terms of an event indicator variable, the triggering information is utilized so as to reduce the conservatism in the filter analysis. An upper bound for the filtering error covariance is obtained in form of Riccati-like difference equations by utilizing the inductive method. Subsequently, such an upper bound is minimized by appropriately designing the filter parameters iteratively, where a novel matrix simplification technique is developed to handle the challenges resulting from the sparseness of the sensor network topology and filter structure preserving issues. The effectiveness of the proposed strategy is illustrated by a numerical simulation.Index Terms-Distributed filtering, wireless sensor networks, eventbased mechanism, Send-on-Delta concept.

Transcranial direct current stimulation (tDCS) targeting the prefrontal cortex reduces the size and speed of standing postural sway in younger adults, particularly when performing a cognitive dual task. Here, we hypothesized that tDCS would alter the complex dynamics of postural sway as quantified by multiscale entropy (MSE). Twenty healthy older adults completed two study visits. Center-of-pressure (COP) fluctuations were recorded during single-task (i.e., quiet standing) and dual-task (i.e., standing while performing serial subtractions) conditions, both before and after a 20-min session of real or sham tDCS. MSE was used to estimate COP complexity within each condition. The percentage change in complexity from single- to dual-task conditions (i.e., dual-task cost) was also calculated. Before tDCS, COP complexity was lower (p = 0.04) in the dual-task condition as compared to the single-task condition. Neither real nor sham tDCS altered complexity in the single-task condition. As compared to sham tDCS, real tDCS increased complexity in the dual-task condition (p = 0.02) and induced a trend toward improved serial subtraction performance (p = 0.09). Moreover, those subjects with lower dual-task COP complexity at baseline exhibited greater percentage increases in complexity following real tDCS (R = −0.39, p = 0.05). Real tDCS also reduced the dual-task cost to complexity (p = 0.02), while sham stimulation had no effect. A single session of tDCS targeting the prefrontal cortex increased standing postural sway complexity with concurrent non-postural cognitive task. This form of noninvasive brain stimulation may be a safe strategy to acutely improve postural control by enhancing the system's capacity to adapt to stressors.

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In this paper, the problems of fault detection, isolation, and estimation are considered for a class of discrete time-varying networked sensing systems with incomplete measurements. A unified measurement model is utilized to simultaneously characterize both the phenomena of multiple communication delays and data missing. A least-squares filter that minimizes the estimation variance is first designed for the addressed time-varying networked sensing systems, and then a novel residual matching (RM) approach is developed to isolate and estimate the fault once it is detected. The RM strategy is implemented via a series of Kalman filters, where each filter is designed to estimate the augmented signal composed of the system state and a specific fault signal. The design scheme for each filter is proposed in a recursive way. The main idea for the fault detection and estimation is that the Kalman filter with least residual value is regarded as corresponding to the right fault signal, and its estimation is utilized to represent the actual occurred fault. The effectiveness of our proposed method is demonstrated via simulation experiments on a real Internet-based three-tank system. Index Terms-Delayed and missing measurements, fault detection and diagnosis (FDD), fault estimation, Kalman filter, networked sensing systems.