The objective of this research is to develop a prototype programmable microfluidic laboratory-on-chip that concurrently executes assays (chemical algorithms) in an on-line fashion. A chemist specifies an assay (chemical algorithm) using a text-based language. Assays arrive at the device in real-time and an operating system/virtual machine running on an attached microcontroller interprets them. The approach is to develop a software simulation infrastructure for the laboratory-on-chip and to build the operating system/virtual machine on top of it. The intellectual merit of this activity is due to the fact that no type of runtime support system has yet been proposed for microfluidic devices. The key challenges to be solved in this project include: deadlock-free deterministic and adaptive routing algorithms; real-time constraints for routing droplets in the system; routing wash droplets for decontamination; scheduling assay operations on the devices; congestion estimation; and fault diagnosis and recovery. In terms of broader impact, advances in laboratory-on-chip technology will improve public health worldwide and lead to significant advances in clinical diagnostics and medicine. Laboratory-on-chips are commercially available from established companies such as Agilent Technologies as well as startup companies such as Advanced Liquid Logic, Silicon Biosystems, and Ayanda Biosystems; thus, the economic impact of this research is tremendous. The University of California, Riverside is a Minority-Serving Institution. The PI is committed to the introduction of laboratory-on-chip technology in both undergraduate and graduate education and will make every possible effort to recruit underrepresented minorities (including women) at the graduate and undergraduate level to work on the project.

The objective of this research is to enable cyberphysical systems (CPS) to be context-aware of people in the environment and to use data from real-world probabilistic sensors. The approach is (1) to use radio tomography (RT) and RFID to provide awareness (location and potential identification) of every person in a building or area, and (2) to develop new middleware tools to enable context-aware computing systems to use probabilistic data, thus allowing new applications to exploit sometimes unreliable estimates of the environment.The intellectual merit of the proposal is in the development of new algorithms and models for building-scale RT with low radio densities and across multiple frequencies; the development of efficient multichannel access protocols for rapid and adaptive peer-to-peer measurements; the development of space-time and probabilistic data representations for use in stream-based context awareness systems and for merging ID and non-ID data; (4) and the development of a human context-aware software development toolkit that interfaces between probabilistic data and context-aware applications. The proposal impacts broadly the area of Cyber-physical systems that reason about human presence and rely on noisy and potentially ambiguous (practical) sensors. The research has additional dramatic impact in: (1) smart facilities which automatically enforce safety, privacy, and security procedures, increasing the ability to respond in emergency situations and prevent accidents and sabotage; (2) elder care, to monitor for physical or social decline so that effective intervention can be implemented, extending the period elders can live in their own home, without pervasive video surveillance.

Using the newly introduced idea of a sensor lattice, this project conducts a systematic study of the "granularity'' at which the world can be sensed and how that affects the ability to accomplish common tasks with cyber-physical systems (CPSs). A sensor is viewed as a device that partitions the physical world states into measurement-invariant equivalence classes, and the sensor lattice indicates how all sensors are related. Several distinctive characteristics of the pursued approach are: 1) Virtual sensor models are developed, which correspond to minimal information requirements of common tasks and are independent of particular physical sensor implementations. 2) Uncertainty is decoupled into disturbances and pre-images, the latter of which yields the measurement-invariant equivalence classes and sensor lattice. 3) The development of particular spatial and temporal filters that are based on minimal information requirements of a task. 4) Formally establishing the conditions that enable sensors in a CPS to be interchanged, and then determining the relative complexity tradeoffs. The intellectual merit is to understand how mappings from the physical world to sensor outputs affect the solvability and complexity of commonly occurring tasks. This is a critical step in the development of mathematical and computational CPS foundations. Broader impact is expected by improving design methodologies for CPS solutions to societal problems such as assisted living, environmental monitoring, and automated agriculture. The sensor lattice approach is transformative because it represents a new paradigm with which to address basic sensor-based inference issues, which extend well beyond the traditional academic boundaries.

Body Area Sensor Networks: A Holistic Approach from Silicon to Users The objective of this research is to develop new principles and techniques for adaptive operation in highly dynamic physical environments, using miniaturized, energy-constrained devices. The approach is to use holistic cross-layer solutions that simultaneously address all aspects of the system, from low-level hardware design to higher-level communication and data fusion algorithms to top-level applications. In particular, this work focuses on body area sensor networks as emerging cyber-physical systems. The intellectual merit includes producing new principles regarding how cyber systems must be designed in order to continually adapt and respond to rapidly changing physical environments, sensed data, and application contexts in an energy-efficient manner. New cross-layer technologies will be created that use a holistic bottom-up and top-down design -- from silicon to user and back again. A novel system-on-a-chip hardware platform will be designed and fabricated using three cutting-edge technologies to reduce the cost of communication and computation by several orders of magnitude. The broad impact of this project will enable the wide range of applications and societal benefits promised by body area networks, including improving the quality and reducing the costs of healthcare. The technology will have broad implications for any cyber physical system that uses energy constrained wireless devices. A new seminar series will bring together experts from many fields (including domain experts, such as physicians and healthcare professionals). The key aspects of this work that deal with healthcare have the potential to attract women and minorities to the computer field.

The objective of this research is to improve the ability to track the orbits of space debris and thereby reduce the frequency of collisions. The approach is based on two scientific advances: 1) optimizing the scheduling of data transmission from a future constellation of orbiting Cubesats to ground stations located worldwide, and 2) using satellite data to improve models of the ionosphere and thermosphere, which in turn are used to improve estimates of atmospheric density. Intellectual Merit Robust capacity-constrained scheduling depends on fundamental research on optimization algorithms for nonlinear problems involving both discrete and continuous variables. This objective depends on advances in optimization theory and computational techniques. Model refinement depends on adaptive control algorithms, and can lead to fundamental advances for automatic control systems. These contributions provide new ideas and techniques that are broadly applicable to diverse areas of science and engineering. Broader Impacts Improving the ability to predict the trajectories of space debris can render the space environment safer in both the near term---by enhancing astronaut safety and satellite reliability---and the long term---by suppressing cascading collisions that could have a devastating impact on the usage of space. This project will impact real-world practice by developing techniques that are applicable to large-scale modeling and data collection, from weather prediction to Homeland Security. The research results will impact education through graduate and undergraduate research as well as through interdisciplinary modules developed for courses in space science, satellite engineering, optimization, and data-based modeling taught across multiple disciplines.

The objective of this research is to improve the ability to track the orbits of space debris and thereby reduce the frequency of collisions. The approach is based on two scientific advances: 1) optimizing the scheduling of data transmission from a future constellation of orbiting Cubesats to ground stations located worldwide, and 2) using satellite data to improve models of the ionosphere and thermosphere, which in turn are used to improve estimates of atmospheric density. Intellectual Merit Robust capacity-constrained scheduling depends on fundamental research on optimization algorithms for nonlinear problems involving both discrete and continuous variables. This objective depends on advances in optimization theory and computational techniques. Model refinement depends on adaptive control algorithms, and can lead to fundamental advances for automatic control systems. These contributions provide new ideas and techniques that are broadly applicable to diverse areas of science and engineering. Broader Impacts Improving the ability to predict the trajectories of space debris can render the space environment safer in both the near term---by enhancing astronaut safety and satellite reliability---and the long term---by suppressing cascading collisions that could have a devastating impact on the usage of space. This project will impact real-world practice by developing techniques that are applicable to large-scale modeling and data collection, from weather prediction to Homeland Security. The research results will impact education through graduate and undergraduate research as well as through interdisciplinary modules developed for courses in space science, satellite engineering, optimization, and data-based modeling taught across multiple disciplines.

The objective of this research is to enable cyberphysical systems (CPS) to be context-aware of people in the environment and to use data from real-world probabilistic sensors. The approach is (1) to use radio tomography (RT) and RFID to provide awareness (location and potential identification) of every person in a building or area, and (2) to develop new middleware tools to enable context-aware computing systems to use probabilistic data, thus allowing new applications to exploit sometimes unreliable estimates of the environment.The intellectual merit of the proposal is in the development of new algorithms and models for building-scale RT with low radio densities and across multiple frequencies; the development of efficient multichannel access protocols for rapid and adaptive peer-to-peer measurements; the development of space-time and probabilistic data representations for use in stream-based context awareness systems and for merging ID and non-ID data; (4) and the development of a human context-aware software development toolkit that interfaces between probabilistic data and context-aware applications. The proposal impacts broadly the area of Cyberphysical systems that reason about human presence and rely on noisy and potentially ambiguous (practical) sensors. The research has additional dramatic impact in: (1) smart facilities which automatically enforce safety, privacy, and security procedures, increasing the ability to respond in emergency situations and prevent accidents and sabotage; (2) elder care, to monitor for physical or social decline so that effective intervention can be implemented, extending the period elders can live in their own home, without pervasive video surveillance.

The objective of this research is to develop non-volatile computing devices, which allow the power source to be cut off at any time, and yet resume regular operation without loss of information when the power comes back. The approach is to replace all critical memory components with non-volatile units so that computing state is maintained over power interruptions. The advancement in new Flash memory devices makes this approach feasible by enabling low-voltage program/erase (P/E) around ±2V and a long (projected >1016) cycling endurance to be integrated into CMOS technology. This research effort seeks to establish a new paradigm of computing where non-volatile memory units are used pervasively to enhance reliability against power source instability, energy-efficiency, and security. The non-volatile computing devices are especially useful for embedded cyber-physical systems enabling long running computations and data collection even with unreliable power sources. The technologies developed from this project can also benefit conventional architecture in its power optimization and internal security code generation. The project is a close collaboration between computer architecture and CMOS technology development groups, where all levels in the design hierarchy will be visited for system and technology evaluation. This project integrates its research efforts with education by developing an undergraduate and Master curriculum that spans over the vertical design hierarchy in microprocessors. This vertical education will better prepare future work force in tackling tremendous design challenges spanning many layers of microprocessors. The results from this project will be made widely available to both industry and academia.

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.

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.