This project has two closely related objectives. The first is to design and evaluate new Cyber Transportation Systems (CTS) applications for improved traffic safety and traffic operations. The second is to design and develop an integrated traffic-driving-networking simulator. The project takes a multi-disciplinary approach that combines cyber technologies, transportation engineering and human factors. While transportation serves indispensible functions to society, it does have its own negative impacts in terms of accidents, congestion, pollution, and energy consumption. To improve traffic safety, the project will develop and evaluate novel algorithms and protocols for prioritization, delivery and fusion of various warning messages so as to reduce drivers? response time and workload, prevent conflicting warnings, and minimize false alarms. To improve traffic operations, the project will focus on the design of next generation traffic management and control algorithms for both normal and emergency operations (e.g. during inclement weather and evacuation scenarios). Both human performance modeling methods and human subjects? experimental methods will be used to address the human element in this research. As the design and evaluation of CTS applications requires an effective development and testing platform linking the human, transportation and cyber elements, the project will also design and develop a simulator that combines the main features of a traffic simulator, a networking simulator and a driving simulator. The integrated simulator will allow a human driver to control a subject vehicle in a virtual environment with realistic background traffic, which is capable of communicating with the driver and other vehicles with CTS messages. Background traffic will be controlled by a realistic driver model based on our human factors research that accounts for CTS messages? impact on driver behavior. Intellectual Merits: The project explicitly considers human factors in the design and evaluation of CTS safety and operations applications, a topic which has not received adequate attention. Moreover, the proposed integrated simulator represents a first-of-a-kind simulator with unique features that can reduce the design and evaluation costs of new CTS applications. Broader Impacts: The proposed research can improve the safety, efficiency and environmental-friendless of transportation systems, which serve as the very foundation of modern societies and directly affects the quality of life. The integrated simulator will be used as a tool for teenage and elderly driver education and training, and to inspire minority, middle and high school students to pursue careers in math, science, and computer-related fields

The objective of this research is to define programming abstractions with temporal semantics for distributed cyber-physical systems. The approach is to create a coordination language for distributed embedded software that blends naturally with models of physical dynamics. The coordination language is based on a rigorous discrete-event concurrent model of computation. It will be used by system designers to construct models from which software implementations are derived. The objective is distributed software that, if it compiles for a platform, delivers precisely the temporal semantics specified in the model. Intellectual merit: This project addresses the core abstractions of computing, which throughout the 20th century, have abstracted away time, and of physical dynamics, which have omitted software and network behaviors. For cyber-physical systems, both are inappropriate. This project is developing new time-centric abstractions for software, programming models, analysis techniques, and integration of software and network models with physical dynamics. Broader impacts: Besides the considerable economic and societal impact of CPS in general, the project is expected to have considerable impact on engineering and computer science education. Its focus on engineering applications and on sound computer science methods will erode the boundaries between these disciplines that hamper competitiveness of our students. A new generation of students is needed to dramatically improve our energy efficiency, manufacturing capabilities, transportation efficiency, instrumentation prowess (and hence, scientific knowledge), and infrastructure robustness. Because of the broad societal implications of the work, it will help attract to engineering and computer science a more diverse talent pool.

The objective of this research is to develop new foundations of composition in heterogeneous systems, to apply these foundations in a new generation of tools for system integration, and to validate the results in experiments using automotive and avionics System-of-Systems experimental platforms. The approach exploits simplification strategies: develop theories, methods, and tools to assist in inter-layer decoupling. The research program has three focus areas: (1) theory of compositionality in heterogeneous systems, (2) tools and tool architectures for system integration, and (3) systems/experimental research. The project develops and deploys theories and methods for inter-layer decoupling that prevent or decrease the formation of intractable system-wide interdependences and maintain compositionality at each layer for carefully selected, essential system properties. Compositionality in tools is sought by exploring semantic foundations for model-based design. Systems/experimental research is conducted in collaboration with General Motors Global R&D (GM) and focuses on electric car platforms. The project is contributing to the cost effective development and deployment of many safety and security-critical cyber-physical systems, ranging from medical devices to transportation, to defense and avionics. The participating institutions seek to complement the conventional curriculum in systems science with one that admits computation as a primary concept. The curriculum changes will be aggressively promoted through a process of workshops and textbook preparation.

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 apply grammatical inference models recently developed in the field of linguistics and phonology, as a basis for abstraction, composition, symbolic control, and learning in distributed multi-agent cyber-physical systems. The approach is to map the system dynamics, specifications, and task interdependences to finite abstract models, and then describe the desired behavior of the system in an appropriate grammar that can be decomposed into local agent specifications. In this framework, the agents can learn the behavior of their environment by observing its dynamics, and update their specifications accordingly. The proposed approach to learning in cyber-physical systems, which is based on grammatical inference at a purely discrete level, is a significant departure from current works. Following this approach, one can reason about large-scale processes resulting from event interdependencies between agents, without having to construct large product systems. To realize this plan, specific technical advances on modeling, abstraction, and control synthesis are proposed. Questions related to formally factoring and composing heterogeneous systems are pervasive in the fields of formal languages and computational learning. There are also applications of commercial significance in the area of discovering new azeotropic mixtures based on documented pairs of compounds that are known to have the particular property. Proposed dissemination and outreach activities include the involvement of middle and high school students and teachers, integrated in existing NSF-sponsored programs at the University of Delaware and Boston University.

The objective of this research is to apply grammatical inference models recently developed in the field of linguistics and phonology, as a basis for abstraction, composition, symbolic control, and learning in distributed multi-agent cyber-physical systems. The approach is to map the system dynamics, specifications, and task interdependences to finite abstract models, and then describe the desired behavior of the system in an appropriate grammar that can be decomposed into local agent specifications. In this framework, the agents can learn the behavior of their environment by observing its dynamics, and update their specifications accordingly. The proposed approach to learning in cyber-physical systems, which is based on grammatical inference at a purely discrete level, is a significant departure from current works. Following this approach, one can reason about large-scale processes resulting from event interdependencies between agents, without having to construct large product systems. To realize this plan, specific technical advances on modeling, abstraction, and control synthesis are proposed. Questions related to formally factoring and composing heterogeneous systems are pervasive in the fields of formal languages and computational learning. There are also applications of commercial significance in the area of discovering new azeotropic mixtures based on documented pairs of compounds that are known to have the particular property. Proposed dissemination and outreach activities include the involvement of middle and high school students and teachers, integrated in existing NSF-sponsored programs at the University of Delaware and Boston University.

The objective of this research is to develop an atomic force microscope based cyber-physical system that can enable automated, robust and efficient assembly of nanoscale components such as nanoparticles, carbon nanotubes, nanowires and DNAs into nanodevices. The approach in this project is based on the premise that automated, robust and efficient nanoassembly can be achieved through tip based pushing in an atomic force microscope with intermittent local scanning of nanoscale components. In particular, in order to resolve temporally and spatially continuous movement of nanoscale components under tip pushing, the research is exploring the combination of intermittent local scanning and interval non-uniform rational B-spline based isogeometric analysis in this research. Successful completion of this research is expected to lead to foundational theories and algorithmic infrastructures for effective integration of physical operations (pushing and scanning) and computation (planning and simulation) for robust, efficient and automated nanoassembly. The resulting theories and algorithms will also be applicable to a broader set of cyber physical systems. If successful, this research will lead to leap progress in nanoscale assembly, from prototype demonstration to large-scale manufacturing. Through its integrated research, education and outreach activities, this project is providing experiences and understanding in cyber-physical systems and nanoassembly for students from high schools to graduate schools. The goal is to increase interest in science and engineering among domestic students and therefore strengthen our competitiveness in the global workforce.

The objective of this research is the development of methods and software that will allow robots to detect and localize objects using Active Vision and develop descriptions of their visual appearance in terms of shape primitives. The approach is bio inspired and consists of three novel components. First, the robot will actively search the space of interest using an attention mechanism consisting of filters tuned to the appearance of objects. Second, an anthropomorphic segmentation mechanism will be used. The robot will fixate at a point within the attended area and segment the surface containing the fixation point, using contours and depth information from motion and stereo. Finally, a description of the segmented object, in terms of the contours of its visible surfaces and a qualitative description of their 3D shape will be developed. The intellectual merit of the proposed approach comes from the bio-inspired design and the interaction of visual learning with advanced behavior. The availability of filters will allow the triggering of contextual models that work in a top-down fashion meeting at some point the bottom-up low-level processes. Thus, the approach defines, for the first time, the meeting point where perception happens. The broader impacts of the proposed effort stem from the general usability of the proposed components. Adding top-down attention and segmentation capabilities to robots that can navigate and manipulate, will enable many technologies, for example household robots or assistive robots for the care of the elders, or robots in manufacturing, space exploration and education.

The objective of the research is to develop tools for comprehensive design and optimization of air traffic flow management capabilities at multiple spatial and temporal resolutions: a national airspace-wide scale and one-day time horizon (strategic time-frame); and at a regional scale (of one or a few Centers) and a two-hour time horizon (tactical time-frame). The approach is to develop a suite of tools for designing complex multi-scale dynamical networks, and in turn to use these tools to comprehensively address the strategic-to-tactical traffic flow management problem. The two directions in tool development include 1) the meshed modeling/design of flow- and queueing-networks under network topology variation for cyber- and physical- resource allocation, and 2) large-scale network simulation and numerical analysis. This research will yield aggregate modeling, management design, and validation tools for multi-scale dynamical infrastructure networks, and comprehensive solutions for national-wide strategic-to-tactical traffic flow management using these tools. The broader impact of the research lies in the significant improvement in cost and equity that may be achieved by the National Airspace System customers, and in the introduction of systematic tools for infrastructure-network design that will have impact not only in transportation but in fields such as electric power network control and health-infrastructure design. The development of an Infrastructure Network Ideas Cluster will enhance inter-disciplinary collaboration on the project topics and discussion of their potential societal impact. Activities of the cluster include cross-university undergraduate research training, seminars on technological and societal-impact aspects of the project, and new course development.

The objective of this research is to develop algorithms and software for treatment planning in intensity modulated radiation therapy under assumption of tumor and healthy organs motion. The current approach to addressing tumor motion in radiation therapy is to treat it as a problem and not as a therapeutic opportunity. However, it is possible that during tumor and healthy organs motion the tumor is better exposed for treatment, allowing for the prescribed dose treatment of the tumor (target) while reducing the exposure of healthy organs to radiation. The approach is to treat tumor and healthy organs motion as an opportunity to improve the treatment outcome, rather than as an obstacle that needs to be overcome. Intellectual Merit: The leading intellectual merit of this proposal is to develop treatment planning and delivery algorithms for motion-optimized intensity modulated radiation therapy that exploit differential organ motion to provide a dose distribution that surpasses the static case. This work will show that the proposed motion-optimized IMRT treatment planning paradigm provides superior dose distributions when compared to current state-of-the art motion management protocols. Broader Impact: Successful completion of the project will mark a major step for clinical applications of intensity modulated radiation therapy and will help to improve the quality of life of many cancer patients. The results could be integrated within existing devices and could be used for training of students and practitioners. The visualization software for dose accumulation could be used to train medical students in radiation therapy treatment planning.