The objective of this research is to bring high levels of system reliability and integrity to application domains that cannot afford the cost, power, weight, and size associated with physical redundancy. The approach is to develop complementary monitoring algorithms and novel computing architectures that enable the detection of faults. In particular, there is a significant opportunity to reduce the reliance on physical redundancy by combining model-based and data-driven monitoring techniques. Implementing this approach to fault detection would be difficult with existing software and computing architectures. This motivates the development of a general purpose monitoring framework through monitoring-aware compilers coupled with enhancements to multi-core architectures. The intellectual merit of the project is twofold. First, it has the potential to lead to a novel fault detection approach that blends complementary monitoring algorithms. Second, advances in multi-core processors are leveraged to enable implementation of these fault detection approaches. This addresses key themes in cyber-physical systems by investigating the fundamental issue of fault detection for physical systems and by developing a generic processor architecture for monitoring. With respect to broader impact, project offers the potential for positive influences on industrial practice and education. If successful, the design ideas from this project can be incorporated into low-cost multi-core architectures suitable for embedded systems. The potentially transformative performance improvement offered by this framework could also impact current research in run-time verification and on-line monitoring. The research is to be incorporated into the course "Design, Build, Simulate, Test and Fly Small Uninhabited Aerial Vehicles" for senior undergraduate and first-year graduate students.

The objective of this research is to develop energy-efficient integrity establishment techniques for dynamic networks of cyber physical devices. In such dynamic networks, devices connect opportunistically and perform general-purpose computations on behalf of other devices. However, some devices may be malicious in intent and affect the integrity of computation. The approach is to develop new trust establishment mechanisms for dynamic networks. Existing trusted computing mechanisms are not directly applicable to cyber physical devices because they are resource-intensive and require devices to have special-purpose hardware. This project is addressing these problems along three research prongs. The first is a comprehensive study of the resource bottlenecks in current trust establishment protocols. Second, the insights from this study are being used to develop resource-aware attestation protocols for cyber physical devices that are equipped with trusted hardware. Third, the project is developing new trust establishment protocols for cyber physical devices that may lack trusted hardware. A key outcome of the project is an improved understanding of the tradeoffs needed to balance the concerns of security and resource-awareness in dynamic networks. Dynamic networks allow cyber physical devices to form a highly-distributed, cloud-like infrastructure for computations involving the physical world. The trust-establishment mechanisms developed in this project encourage devices to participate in dynamic networks, thereby unleashing the full potential of dynamic networks. This project includes development of dynamic networking applications, such as distributed gaming and social networking, in undergraduate curricula and course projects, thereby fostering the participation of this key demographic.

The objective of this research is to discover new fundamental principles, design methods, and technologies for realizing distributed networks of sub-cm3, ant-sized mobile micro-robots that self-organize into cooperative configurations. The approach is intrinsically interdisciplinary and organized along four main thrusts: (1) Algorithms for distributed coordination and control under severe power, communication, and mobility constraints. (2) Electronics for robot control using event-based communication and computation, ultra-low-power radio, and adaptive analog-digital integrated circuits. (3) Locomotion devices and efficient actuators using rapid-prototyping and MEMS technologies that can operate robustly under real-world conditions. (4) Integration of the algorithms, electronics, and actuators into a fleet of ant-size micro-robots. No robots at the sub-cm3 scale exist because their development faces a number of open challenges. This research will identify and determine means for solving these challenges. In addition, it will provide new solutions to outstanding questions about resource-constrained algorithms, architectures, and actuators that can be widely leveraged in other applications. The PIs will adopt a co-design philosophy that promotes cross-disciplinary research and tight collaboration. Networks of ant-sized robots are expected to be useful in disaster relief, manufacturing, warehouse management, and ecological monitoring, as well as in new unforeseen applications. In addition, the new methods and principles proposed here can be transitioned to other highly-distributed and resource-constrained engineering problems, such as air-traffic control systems. This research program will train Ph.D. students with unique skills in the design of hybrid distributed networks and it will involve undergraduate students, particularly underrepresented minorities and women.

The objective of this research is to create interfaces that enable people with impaired sensory-motor function to control interactive cyber-physical systems such as artificial limbs, wheelchairs, automobiles, and aircraft. The approach is based on the premise that performance can be significantly enhanced merely by warping the perceptual feedback provided to the human user. A systematic way to design this feedback will be developed by addressing a number of underlying mathematical and computational challenges. The intellectual merit lies in the way that perceptual feedback is constructed. Local performance criteria like stability and collision avoidance are encoded by potential functions, and gradients of these functions are used to warp the display. Global performance criteria like optimal navigation are encoded by conditional probabilities on a language of motion primitives, and metric embeddings of these probabilities are used to warp the display. Together, these two types of feedback facilitate improved safety and performance while still allowing the user to retain full control over the system. If successful, this research could improve the lives of people suffering from debilitating physical conditions such as amputation or stroke and also could protect people like drivers or pilots that are impaired by transient conditions such as fatigue, boredom, or substance abuse. Undergraduate and graduate engineering students will benefit through involvement in research projects, and K-12 students and teachers will benefit through participation in exhibits presented at the Engineering Open House, an event hosted annually by the College of Engineering at the University of Illinois.

The objective of this research is to develop a theory of ActionWebs, that is, networked embedded sensor-rich systems, which are taskable for coordination of multiple decision-makers. The approach is to first identify models of ActionWebs using stochastic hybrid systems, an interlinking of continuous dynamical physical models with discrete state representations of interconnection and computation. Second, algorithms will be designed for tasking individual sensors, based on information objectives for the entire system. Third, algorithms for ActionWebs will be developed using multi-objective control methods for meeting safety and efficiency objectives. Two grand challenge applications for this research are in Intelligent Buildings for optimal heating, ventilation, air conditioning, and lighting based on occupant behavior and external environment; and Air Traffic Control for mobile vehicle platforms with sensor suites for environmental sensing to enable safe, convenient, and energy efficient routing. The intellectual merit of this research stems from a conceptual shift of ActionWebs away from passive information gathering to an action-orientation. This involves: modeling of ActionWebs using stochastic hybrid systems; taskable, multi-modal, and mobile sensor webs; and multi-scale action-perception hierarchies. The broader impact of the research is in two grand challenge national problems: energy efficient air transportation, and energy efficient, high productivity buildings, and will tackle social, privacy, economic, and usability issues. Integrated with the research is a program of coursework development in networked embedded systems, across stove pipes in EECS, Aero-Astro, Civil, and Mechanical Engineering departments. Outreach objectives include new course design at San Jose State University, and recruiting more women researchers.

The objective of this research is to develop new principles for creating and comparing models of skilled human activities, and to apply those models to systems for teaching, training and assistance of humans performing these activities. The models investigated will include both hybrid systems and language-based models. The research will focus on modeling surgical manipulations during robotic minimally invasive surgery. Models for expert performance of surgical tasks will be derived from recorded motion and video data. Student data will be compared with these expert models, and both physical guidance and information display methods will be developed to provide feedback to the student based on the expert model. The intellectual merit of this work lies in the development of a new set of mathematical tools for modeling human skilled activity. These tools will provide new insights into the relationship between skill, style, and content in human motion. Additional intellectual merit lies in the connection of hybrid systems modeling to language models, the creation of techniques for automated training, and in the assessment of new training methods. The broader impact of this research will be the creation of automated methods for modeling and teaching skilled human motion. These methods will have enormous implications for the training and re-training of the US workforce. This project will also impact many diversity and outreach activities, including REU programs and summer camps for K-12 outreach. The senior personnel of this project also participate in the Robotic Systems Challenge and the Women in Science and Engineering program.

The objective of this research is to develop principles and tools for the design of control systems using highly distributed, but slow, computational elements. The approach of this research is to build an architecture that uses highly parallelized, simple computational elements incorporating nonlinearities, time delay and asynchronous computation as integral design elements. Tools for the design of non-deterministic protocols will be developed and demonstrated using an existing multi-vehicle testbed at Caltech. The motivation for using "slow computing" is to develop new feedback control architectures for applications where computational power is extremely limited. Examples of such systems are those where the energy usage of the system must remain small, either due to the source of power available (e.g. batteries or solar cells) or the physical size of the device (e.g. microscale and nanoscale robots). A longer term application area is in the design of control systems using novel computing substrates, such as biological circuits. A critical element in both cases is the tight coupling between the dynamics of the underlying process and the temporal properties of the algorithm that is controlling it. The implementation plan for this project involves students from multiple disciplines (including bioengineering, computer science, electrical engineering and mechanical engineering) as well as at multiple experience levels (sophomores through PhD students) working together on a set of interlinked research problems. The project is centered in the Control and Dynamical Systems department at Caltech, which has a strong record of recruiting women and underrepresented minority students into its programs.

The objective of this research is to enable improved performance and application development efficiency for streaming applications. The approach is to use architecturally diverse computing engines, such as field programmable gate arrays and graphics processing units, to execute portions of an application. This approach is especially well suited to applications that process streaming data, such as from a sensor array like a telescope or other scientific instrument. Intellectual merit. While the use of architecturally diverse systems has a long history in the world of performance-critical systems that interact with the physical world, application development has always been a challenge. This proposal seeks to improve application development by making significant progress in two essential areas: performance monitors and debuggers. The approach has the following properties: it is non-intrusive with respect to the application being monitored and it supports data collection concerning rare events. In particular, it allows correlation of the cyber portion of an application with the physical portion in which the rare events occur. Broader impacts. Impact on society: The research can have a dramatic impact on cyber-physical computing applications, from scientific instrumentation to medical imaging. Improvements in power efficiency, weight, and volume are all achievable through the use of architectural diversity. Education: The proposed research will be carried out by graduate and undergraduate students, both furthering their education and increasing the nation's trained workforce. Underrepresented groups: Washington University's Chancellor's Fellowship and Olin Fellowship programs will be leveraged to attract the participation of individuals from underrepresented groups.

The objective of this research is to develop an integrated methodology for control system design in situations where disturbances primarily result from routine human behavior, as, for example, in future artificial pancreas systems where meals and exercise are the main disturbances affecting blood glucose concentration. The approach is to recognize that human behavioral disturbances (i) are generally random but cannot be treated as zero-mean white noise processes and (ii) occur with statistical regularity but cannot be treated as periodic due to natural variation in human behavior. This emerging class of problems requires (i) the derivation of new mathematical representations of disturbances for specific applications and (ii) the formulation of new stochastic control models and algorithms that exploit statistical regularity in the disturbance process. The intellectual merit of the proposed research stems from the fact that it explicitly recognizes a new class of disturbances, human behavioral disturbances, seeking to develop an integrated approach to statistically characterizing and responding to future perturbations, adapting gracefully to uncertainty about the future. The anticipated research outcomes will be relevant in diverse fields, including stochastic hybrid control and human automation interaction. As a broader implication, the proposed research will enable the design of future field deployable artificial pancreas systems, potentially improving the lives of 1.5 million Americans suffering from Type 1 diabetes. With help from the two graduate students funded by the project, the principle investigator will supervise a Capstone design course, exposing undergraduates to various aspects of control under human behavioral disturbances.

This objective of this proposal is to improve the management of the air traffic system, a cyber-physical system where the need for a tight connection between the computational algorithms and the physical system is critical to safe, reliable and efficient performance. The approach is based on an adaptive multiagent coordination algorithm with a particular emphasis on the systematic selection of the agents, their actions and the agents' reward functions. The intellectual merit lies in addressing the agent coordination problem in a physical setting by shifting the focus from "how to learn" to "what to learn." This paradigm shift allows a separation between the learning algorithms used by agents, and the reward functions used to tie those learning systems into system performance. By exploring agent reward functions that implicitly model agent interactions based on feedback from the real world, this work aims to build cyber-physical systems where an agent that learns to optimize its own reward leads to the optimization of the system objective function. The broader impact is in providing new air traffic flow management algorithms that will significantly reduce air traffic congestion. The potential impact cannot only be measured in currency ($41B loss in 2007) but in terms of improved experience by all travelers, providing a significant benefit to society. In addition, the PIs will use this project to train graduate and undergraduate students (i) by developing new courses in multiagent learning for transportation systems; and (ii) by providing summer internship opportunities at NASA Ames Research Center.