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We present an algorithmic framework for learning local causal structure around target variables of interest in the form of direct causes/effects and Markov blankets applicable to very large data sets with relatively small samples. The selected feature sets can be used for causal discovery and classification. The framework (Generalized Local Learning, or(More)
Building networked control systems over wireless networks is an extremely challenging task, as the wireless communication characteristics such as random packet losses and delay, significantly affect the stability and the performance of the control systems. We present a novel approach to the design of wireless networked control system. This approach(More)
An increasing number of distributed real-time systems face the critical challenge of providing quality of service guarantees in open and unpredictable environments. In particular, such systems often need to enforce utilization bounds on multiple processors in order to avoid overload and meet end-to-end deadlines even when task execution times are(More)
—This paper addresses the problem of resilient in-network consensus in the presence of misbehaving nodes. Secure and fault-tolerant consensus algorithms typically assume knowledge of nonlocal information; however, this assumption is not suitable for large-scale dynamic networks. To remedy this, we focus on local strategies that provide resilience to faults(More)
In part I of this work we introduced and evaluated the Generalized Local Learning (GLL) framework for producing local causal and Markov blanket induction algorithms. In the present second part we analyze the behavior of GLL algorithms and provide extensions to the core methods. Specifically, we investigate the empirical convergence of GLL to the true local(More)
Researchers in the diagnosis community have developed a number of promising techniques for system health management. However, realistic empirical evaluation and comparison of these approaches is often hampered by a lack of standard data sets and suitable testbeds. In this paper we describe the Advanced Diagnostics and Prognostics Testbed (ADAPT) at NASA(More)
Multiple fault diagnosis is a challenging problem because the number of candidates grows exponentially in the number of faults. In addition, multiple faults in dynamic systems may be hard to detect, because they can mask or compensate each other's effects. The multiple fault problem is important, since the single fault assumption can lead to incorrect or(More)
Invited Paper In this paper, the supervisory control of hybrid systems is introduced and discussed at length. Such control systems typically arise in the computer control of continuous processes, for example, in manufacturing and chemical processes, in transportation systems , and in communication networks. A functional architecture of hybrid control(More)
Modern Network Centric Operations drive the complexity of Information Fusion and Command and Control (C2) Systems. Driving this complexity further is the interplay dynamics of the human element, information systems, and communication networks. The lack of low-cost realistic experimental context limits the testing, evaluation, and further development of(More)
In this paper, a novel methodology for analysis of piecewise linear hybrid systems based on discrete abstractions of the continuous dynamics is presented. An important characteristic of the approach is that the available control inputs are taken into consideration in order to simplify the continuous dynamics. Control specifications such as safety and(More)