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Research in Signal ProcessingCognitive Networks Global Behavior Sensor Networks Time Reversal Imaging Time Reversal Infrastructure Health Monitoring Atomic Force Microscopy: MOSAIC |
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Network Science: Cognitive NetworksNSF CIF: Large: Collaborative Research: Cooperation and Learning over Cognitive Networks (project start date - Spetember 1st, 2010) Abstract. Studies on herding in economics and the social and biological sciences have observed that coordination among multiple agents leads to regular patterns of behavior and swarm intelligence, even when each group member shows very limited behavioral complexity. In ant colonies, for example, individual ants cannot capture rich spatial information from their environment because of their limited, localized sensing ability. Nevertheless, when the ants coordinate their activities together within a colony, the group ends up exhibiting better sensing abilities. Can one explain how and why such manifestations of rational behavior arise at the group level from the interactions of agents with limited individual abilities at the local level? What communication topologies enable such behavior and how much information quantization is performed at the local level? Likewise, self-organization is a remarkable property of nature and it has been observed in several physical and biological systems. Examples include fish joining together in schools, chemicals forming spirals, and sand grains assembling into rippling dunes. In self-organizing systems, a global pattern emerges from the interaction of the individual components of the system. For example, flocks of birds self-organize into V-formations when they need to travel long distances. What type of coordination is employed by the birds to get into this formation? How can other formation topologies be justified? What type of communication patterns enables such formations? Interestingly, a close synergy is evolving between studies on herding and flocking in the social and biological sciences and recent developments in the signal processing and communications communities on cognitive networks. These networks avoid centralized information processing and perform in-network inference and control decisions without relying on fusion centers. This is because solutions that rely on information fusion are not scalable, are hard to adapt to changing network conditions, and create single points of vulnerability and information bottlenecks. Objectives. The research proposes to exploit the connection between socio-economic-biological networks and cognitive networks in an effort to understand and reverse-engineer the decentralized intelligence encountered in the biological and socio-economic domains. What distinguishes this work from prior efforts is its focus on understanding how learning and rationality evolve from low-level interactions on one hand, and how network dynamics and mobility relate to optimality on the other hand. Students collaborating in this project:Faculty collaborations:
Network Science: Global BehaviorAFOSR: Global Behavior in Large Scale Systems (project start date - June 1st, 2010) Abstract. We consider fusion of information collected by a large scale, distributed, sparsely connected network of sensing agents, sensing centers, or sensors monitoring a large collection of dynamical (possibly stochastic) sparsely coupled systems. Examples include large critical infrastructures such as the power grid, the telecom wireless infrastructure, or an information network. We develop new methodologies to study the global behavior of these large scale networked systems that emerges from a multitude of local actions (events) and to study fusion (e.g., detection, estimation) under their prevailing operation conditions, e.g., besides measurement and communication noise, the intermittency of infrastructure failures. There are a number of challenges: 1) the large scale of the system makes it not practical to have complete model descriptions that account with minutiae for all the local (component) dynamics as well as for their complex interactions; 2) these systems are subject to rare but catastrophic events, like a continental size blackout, a system wide attack that spirals up to overpower the system resources, or service delays that cascade out of control; 3) often, large scale networked systems exhibit a phase change, i.e., below critical values of a set of parameters, the system is well behaved, while above these critical values the system becomes unstable or collapses; and finally, 4) the amount of data potentially collected over time and from all the monitoring centers may be staggering, making it unfeasible to process centrally at a fusion center. We develop methods that can handle many of these challenges. Our approach emphasizes the‘largeness’ of the system and focus on asymptotic behavior. Surprisingly, it allows understanding the global behavior that emerges from the many local interactions and design strategies for appropriate provisioning of resources to avoid or prevent out of control undesirable behaviors. We capture these questions in the context of stochastic networks and their limiting behavior, e.g., obtained by studying their fluid limit and large deviation principle. We model the large scale system by a network of dynamic nodes and abstract from the physics of the system the events of interest (unserviced calls, or system attacks). We model the local requests or events as point processes, their propagation across the network by rate parameters, and the state of the system by a jump Markov process. To study the system behavior, we focus on a global entity, the empirical distribution of the state. To study the emergent system behavior, we consider normalized versions of the system under appropriate scalings. This approach allows addressing different questions–e.g., switching among different stability regions, (multistability), occurrence of phase change behavior, or rate at which catastrophic behavior emerges. To process the data, we study distributed inference under appropriate system conditions, like system failures. Our work develops models, analysis methodologies, and signal and information processing techniques that are needed to address global issues regarding large scale networked systems. Beyond moment (mean or second order) analysis, it focus on sample path behavior and large deviation principle to determine rates at which rare, but catastrophic, behaviors occur. These methods, not yet commonly used by researchers in these areas, are the appropriate tools to handle the large scale of these systems. The foundational nature of our work applies across a number of systems including the power grid, telecom wireless networks, or cyber-security infrastructures, e.g., botnets of compromised computers. We propose to characterize the rate at which rare, but catastrophic, events occur, so operators can provision the system (e.g., installed capacity) so that these rates are well within the safety guarantees adopted by the decision or policy makers. Our work will lead to better understanding of how to fuse the distributed information collected from a large scale system to infer their global behaviors. Students collaborating in this project:
Sensor NetworksThe potential for large-scale surveillance systems has attracted attention in recent years due to emerging technological advancements. The increasing levels of integration as well as the development of robust signal processing algorithms lend themselves to the deployment of affordable yet reliable sensing systems, which are envisioned as networks of autonomous densely distributed sensor nodes. Power and bandwidth scarcities make the design of such networks a delicate task, involving a careful balance between competing goals and objectives, which has been the subject of research from a variety of perspectives. One viewpoint emphasizes the communication and networking issues such as, routing protocols, networking architectures, and transmission technologies. Our viewpoint focuses on distributed inference algorithms like detection, estimation, localization. At different times Faculty collaborations have included:
Students involved in distributed algorithms and sensor networks research:
Students who graduated:
Research in sensor networks
Some Early Seminars on Sensor Networks:
Others:
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Time Reversal Imaging Laboratory (TriLab)Time reversal techniques obtain increased resolution by exploiting scattering and multipath in propagation through inhomogeneous channels. Time reversal has been used by Fink and collaborators to achieve super-resolution focusing in acoustics, [Fink, Prada, Wu, Cassereau, 1989,Fink, 1997] as demonstrated by their work with controlled ultrasonic experiments in water tanks. More recently large-scale acoustics experiments in the ocean have confirmed the resolution ability of time reversal, [Kuperman, Hodgkiss, and Song, 1998,Song, Kuperman, Hodgkiss, Akal, and Ferla, 1999]. In our work, we study matched field detection with time reversal in the electromagentic (EM) domain. In classical matched field processing (MFP), in the acoustical domain, e.g., [Baggeroer, Kuperman, Mikhalevsky, 1983], detailed modeling of the channel is used to predict the field as received by an array of sensors, after the wavefield propagates through an inhomogenous channel. MFP, in simple terms, solves an inverse problem (source detection or location) by steping through a sequence of forward problems, where in each forward problem the unknown location of the source is postulated at each one of potential positions. Practical implementation of MFP implies the solution of the wave equation for each forward problem assuming a given channel velocity propagation profile and given boundary conditions. This is computationaly demanding and requires good knowledge of the environmental conditions, both of which make MFP an expensive, sensitive solution for many practical problems. Time reversal provides a very good alternative to MFP, since it avoids the detailed modeling of the channel, while still providing the potential gain from matching to the propagated field, rather than matching to the original transmitted wavefield. In a sense, time reversal provides the actual channel Greens' function, in contrast with MFP where the channel Greens' function is computed from the model. Work sponsored by DARPA DSO Advanced Mathematics Computational Program Initiative on Time Reversal Imaging through Army Research Office grant ARO W911NF-04-1-0031. Faculty collaborations in this project:Staff researcher affiliated with this project:
Students collaborating in this project:
Publications on time reversal:
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Time Reversal: Infrastructure Health MonitoringOver the last decade, the deterioration of the United
State’s infrastructure has become a subject of increasing
concern. Pipeline networks are particularly difficult
to maintain. The United States has approximately
305,000 miles of interstate and intrastate pipelines dedicated Ultrasonic guided wave technology has become an important tool for evaluating the integrity of many civil structures. Guided waves can travel long distances in many materials and propagate through the entire thickness of the object under test. These properties make them appealing for nondestructive inspection. Many ultrasonic pipeline inspection systems use large rings of transducers to excite guided waves and listen for echoes produced by cracks, corrosion, or other damage. In a structural health monitoring environment, transducers are permanently attached to the structure. These systems can be used to monitoring large networks of pipelines in real-time. Processing techniques can also take advantage of the transducers’ spatial diversity to improve detection performance, while reducing power requirements. We have demonstrated a monitoring technique using Time Reversal Change Focusing (TRCF). In time reversal focusing, a response between some source and an array receiver is obtained. A time-reversed version of the response is then propagated backward through the medium from the same array. These waves propagate as if traveling backward in time and focus spatially and temporal back at an original source. Time reversal focusing has been extensively investigated for pulse-echo ultrasonic inspection. In TRCF, changes in medium caused by damage can be illuminated using time reversal focusing. This project is supported by a grant of DOE through NETL. CMU is a subcontractor to Concurrent Technologies Corporation (CTC), Johnstown, PA. Industry collaboration:
Faculty collaborations in this project:Also, at University of Maryland, Eastern Shore:
Students collaborating in this project:
Publications on infrastructure health monitoring: Ultrasound time reversal pipeline monitoring
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Atomic Force Microscopy: MOSAICComing soon.
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Last updated 02 April 2004.