The paper presents a prototype wireless system for the detection of active fatigue cracks in aging railways bridges in real-time. The system is based on a small low-cost sensor node, called an AEPod, that has four acoustic emission (AE) channels and a strain channel for sensing, as well as the capability to communicate in a wireless fashion with other nodes and a base station. AEPods are placed at fracture-critical bridge locations. The strain sensor detects oncoming traffic and triggers the AEPod out of its hibernation mode. As the train stresses the fracture-critical member, acoustic emission and strain data are acquired. The data are compressed and filtered at the AEPod and transmitted off the bridge using cell-phone communication.
To support real world applications, a multi-agent system must deal with all relevant issues from the environment, the problem, the users and the technological infrastructure. The real world introduces restrictions and noise at all levels. The communication network may provide intermittent connectivity, variable latency and limited bandwidth. Devices have limited computation. The world is uncertain and dynamic. Tasks, resources and the team objective can change. The software may give incorrect advice. Users can make errors and initiate unplanned actions.
Using evidence-based guidelines to standardize the care of patients with complex medical problems is a difficult challenge. In acute care settings, such as intensive care units, the inherent problems of stabilizing and improving vital patient parameters is complicated by the division of responsibilities among different healthcare team members. Computerized support for implementing such guidelines has tremendous potential. The use of model-integrated techniques for specifying and implementing guidelines as coordinated asynchronous processes is a promising new methodology for providing advanced clinical decision support. Combined with visual dashboards, which show the status of the implemented guidelines, a new approach to computer-supported care is possible. The Vanderbilt Medical Center is applying these techniques to the management of sepsis.
Fault-tolerant frameworks for large scale computing clusters require sensor programs, which are executed periodically to facilitate performance and fault management. By construction, these clusters use general purpose operating systems such as Linux that are built for best average case performance and do not provide deterministic scheduling guarantees. Consequently, periodic applications show jitter in execution times relative to the expected execution time. Obtaining a deterministic schedule for periodic tasks in general purpose operating systems is difficult without using kernel-level modifications such as RTAI and RTLinux. However, due to performance and administrative issues kernel modification cannot be used in all scenarios. In this paper, we address the problem of jitter compensation for periodic tasks that cannot rely on modifying the operating system kernel. ; Towards that, (a) we present motivating examples; (b) we present a feedback controller based approach that runs in the user space and actively compensates periodic schedule based on past jitter; This approach is platform-agnostic i.e. it can be used in different operating systems without modification; and (c) we show through analysis and experiments that this approach is platform-agnostic i.e. it can be used in different operating systems without modification and also that it maintains a stable system with bounded total jitter.
An introduction to the fundamental issues and limitations of communication and networking in automation is given. Digital communication fundamentals are reviewed and networked control systems together with teleoperation are discussed. Issues in both wired and wireless networks are presented.
This paper introduces a novel method for bearing estimation based on a rotating antenna generating a Doppler shifted RF signal. The small frequency change can be measured even on low cost resource constrained nodes using a radio interferometric technique introduced previously. Measuring the Doppler shift at two known locations provides a bearing estimate to the rotating node. An alternative approach employing a switched antenna array is proposed that provides improved robustness by avoiding moving parts.
This paper introduces the larger features of the Cell Processor that allow this specialized hardware architecture to provide a significant amount of increased performance. Specialized configurations call for specialized programming in order to harness the available performance increase. Such high computation configurations are prime targets for signal processing applications. There exists a tool set for modeling the dataflow of a signal processing application. A major goal exists to allow for generation of code to be used on the Cell. The first step involves learning the required techniques for programming by way of porting an example application to the Cell. This paper shows the first steps of utilizing the multi-core architecture which yields a significant increase in performance with room for further improvement in the future.
Sensor webs are often composed of servers connected to distributed real-time embedded (DRE) systems that operate in open environments where operating conditions, workload, resource availability, and connectivity cannot be accurately characterized a priori. The South East Alaska MOnitoring Network for Science, Telecommunications, Education, and Research (SEAMONSTER) project exhibits many common system management and dynamic operation challenges for effective, autonomous system adaptation in a representative sensor web. These challenges cover both field operation (e.g., power management through system sleep/wake cycles and reaction to local environmental changes) and server operation (e.g., system adaptation for new/modified goals, resource allocation for a changing set of applications, and configuration changes for fluctuating workload). This paper presents the results of integrating and applying quality-of-service (QoS)-enabled component middleware, dynamic resource management, and autonomous agent technologies to address these challenges in SEAMONSTER.
Collaborative localization and discrimination of multiple acoustic sources is an important problem in Wireless Sensor Networks (WSNs). Localization approaches can be categorized as signal-based and feature-based methods. The signal-based methods are not suitable for collaborative localization in WSNs because they require transmission of raw acoustic data. In feature-based methods, signal features are extracted at each sensor and the localization is done by multisensor fusion of the extracted features. Such methods are suitable for WSNs due to their lower bandwidth requirements. In this paper, we present a feature-based localization and discrimination approach for multiple harmonic acoustic sources in WSNs. The approach uses acoustic beamform and Power Spectral Density (PSD) data from each sensor as the features for multisensor fusion, localization, and discrimination. We use a graphical model to formulate the problem, and employ maximum likelihood and Bayesian estimation for estimating the position of the sources as well as their fundamental and dominant harmonic frequencies. We present simulation and experimental results for source localization and discrimination, to demonstrate our approach. In our simulations, we also relax the source assumptions, specifically the harmonic and omnidirectional source assumptions, and evaluate the effect on localization accuracy. The experimental results are obtained using motes equipped with microphone arrays and an onboard FPGA for computing the beamform and the PSD.
Distributed sensor Webs typically operate in dynamic environments where operating conditions, transient phenomena, availability of resources, and network connection quality change frequently and unpredictably. Often these changes can neither be completely anticipated nor accurately described during development or deployment. Our prior work has described how we developed agents and services that are capable of monitoring these changing conditions and adapting system parameters using the CORBA component model (CCM) deployment infrastructure as part of the multiagent architecture for coordinated responsive observations (MACRO) platform. Our recent application of MACRO to the South East Alaska monitoring network for Science, Telecommunications, Education, and Research (SEAMONSTER) project has identified new distributed deployment infrastructure challenges common to computationally constrained field environments in adaptive sensor Webs. These challenges include standardized execution of low-level hardware-dependent actions and on-going data tasks, automated provisioning of agents for heterogeneous field hardware, and minimizing deployment infrastructure overhead. This paper describes how we extended MACRO to address these sensor Web challenges by creating an action/effector framework standardizing the execution of lightweight actions and providing for automated provisioning of MACRO agents, in addition to footprint optimizations to the underlying CCM infrastructure.