Urban communities are facing many challenges due to the increasing complexity of urban life, declining urban services and growing health and economic disparities. While diverse stakeholders are engaged in understanding and solving these issues, progress has not been commensurate with the effort, attributed partially to the limited collaboration and data sharing. The persistence of obesity disparities in early childhood is one example of the negative consequences of such isolated efforts. Obesity is a multi-faced health outcome. While some risk-factors for obesity are universal, others are highly specific to the community in which a particular child lives. As such, successful efforts to prevent and treat pediatric obesity depend upon integration of data from multiple community sources and systems. The overall objective of the proposed research is to develop an innovative data-driven health informatics system (Preschool Risk for Obesity Portal; PROP) that aims to promote comprehensive, efficient, and personalized obesity-related care for preschoolers living in urban communities. Through the data sharing and integration within the community and the development, along with the beta-test of PROP, the project has the potential to promote a healthier urban community. Through the data sharing and integration within the community and the development, along with the beta-test of PROP, the project has the potential to promote a healthier urban community. The approach taken could be adapted for older pediatric age-groups, adults, and to address other health disparity issues in urban communities. From a technical perspective, the PIs will: 1) design innovative multi-level mixed effects machine learning methods and scalable algorithms that can precisely identify and prioritize a preschooler's personalized risk factors for obesity and 2) develop a data- and tool-rich online system dedicated to pediatric obesity. Specifically, design (Phase one) and proof-of-concept testing (Phase two) for the PROP algorithms will be completed in this exploratory work. After the successful completion, the second component of PROP (an eHealth intervention) will be developed in a separate, bigger project for efficacy trial (Phase three) and effectiveness research (Phase four). The significant intellectual merit of this project lies in the novel algorithms for information extraction and understanding from multi-scale, correlated, and heterogeneous datasets. The online system dedicated to pediatric obesity will be built for the rapid dissemination of core computational techniques to researchers.

1637258 / 1637249 Yu, Nanpeng / Dong, Bing By 2050, 70% of the world's population is projected to live and work in cities, with buildings as major constituents. Buildings' energy consumption contributes to more than 70% of electricity use, with people spending more than 90% of their time in buildings. Future cities with innovative, optimized building designs and operations have the potential to play a pivotal role in reducing energy consumption, curbing greenhouse gas emissions, and maintaining stable electric-grid operations. Buildings are physically connected to the electric power grid, thus it would be beneficial to understand the coupling of decisions and operations of the two. However, at a community level, there is no holistic framework that buildings and power grids can simultaneously utilize to optimize their performance. The challenge related to establishing such a framework is that building control systems are neither connected to, nor integrated with the power grid, and consequently a unified, global optimal energy control strategy at a smart community level cannot be achieved. Hence, the fundamental knowledge gaps are (a) the lack of a holistic, multi-time scale mathematical framework that couples the decisions of buildings stakeholders and grid stakeholders, and (b) the lack of a computationally-tractable solution methodology amenable to implementation on a large number of connected power grid-nodes and buildings. In this project, a novel mathematical framework that fills the aforementioned knowledge gaps will be investigated, and the following hypothesis will be tested: Connected buildings, people, and grids will achieve significant energy savings and stable operation within a smart city. The envisioned smart city framework will furnish individual buildings and power grid devices with custom demand response signals. The hypothesis will be tested against classical demand response (DR) strategies where (i) the integration of building and power-grid dynamics is lacking and (ii) the DR schemes that buildings implement are independent and individual. By engaging in efficient, decentralized community-scale optimization, energy savings will be demonstrated for participating buildings and enhanced stable operation for the grid are projected, hence empowering smart energy communities. To ensure the potential for broad adoption of the proposed framework, this project will be regularly informed with inputs and feedback from Southern California Edison (SCE). In order to test the hypothesis, the following research products will be developed: (1) An innovative method to model a cluster of buildings--with people's behavior embedded in the cluster's dynamics--and their controls so that they can be integrated with grid operation and services; (2) a novel optimization framework to solve complex control problems for large-scale coupled systems; and (3) a methodology to assess the impacts of connected buildings in terms of (a) the grid's operational stability and safety and (b) buildings' optimized energy consumption. To test the proposed framework, a large-scale simulation of a distribution primary feeder with over 1000 buildings will be conducted within SCE?s Johanna and Santiago substations in Central Orange County.

This EArly-Concept Grant for Exploratory Research (EAGER) project will design an analytic model for assessing a community's resilience and analyzing the multi-dimensional effects of a crisis or disaster on the population. The research will provide new insights into network theory and how network characteristics affect transmission of hazard and risk warnings within communities. The outcomes of this effort will provide alternate approaches to planning and response, and develop the foundation for analyzing dynamic changes in social network structure that occur as crises unfold. Project findings will provide first responders, local, and state governments with the capacity to visualize and mobilize their communities and human capital in innovative and effective ways. The project also offers the potential to enhance public and private sector collaboration for disaster planning, build trusted communications networks, and improve coordination of resources across the private sector. The mobilization of human capital is the most challenging facet of any response to a disaster. This research adopts a novel approach for analyzing civil emergencies by addressing the core question of the cost - broadly defined in terms of the negative social, economic and psychological impacts - of a single civil disaster event on a community. The research will employ a mixed-methods, multi-disciplinary approach to conduct a full spectrum of impact analyses on the economic, social, psychological, and security costs of a civil disaster. The impact analyses will be followed by an assessment of the response, resiliency, and adaptability of the community through the integration of the human capital database. Findings from this research could potentially transform analytical approaches in evaluating the response and economic cost analyses of disasters.

Every year around 30,000 fatalities and 2.2 million injuries happen on US roads. The problem is compounded with huge economic losses due to traffic congestions. Advances in Cooperative Vehicle Efficiency and Safety (CVES) systems promise to significantly reduce the human and economic cost of transportation. However, large scale deployment of such systems is impeded by significant technical and scientific gaps, especially when it comes to achieving real-time and high accuracy situational awareness for cooperating vehicles. This CAREER project aims at closing these gaps through developing fundamental information networking methodologies for coordinated control of automated systems. These methodologies will be based on the innovative concept of modeled knowledge propagation. In addition, the educational component of this project integrates interdisciplinary Cyber-Physical Systems (CPS) subjects on the design of automated networked systems into graduate and undergraduate training modules. For robust operation, CVES systems require each vehicle to have reliable real-time awareness of the state of other coordinated vehicles. This project addresses the critical need for robust control-oriented situational awareness by developing a multi-resolution information networking methodology that is model- and context-aware. The approach is to develop the novel concepts of model communication and its derived multi-resolution networking. Context-aware model-communication relies on transmission and synchronization of models (e.g., stochastic hybrid system structures and parameters) instead of raw measurements. This allows for high fidelity synchronization of dynamical models of CVES over networks. Multi-resolution networking concept is enabled through scalable representations of models. Multi resolution models allow in-network adaptation of model fidelity to available network resources. The result is robustness of CVES to network service variability. The successful deployment of CVES, even partially, will provide significant societal benefits through reduced traffic accidents and improved efficiency. This project will enable large scale CVES deployment by addressing its scalability challenge. In addition, methodologies developed in this project will be crucial to emerging autonomous vehicles, which are also expected to coordinate their actions over communication networks. The fundamental research outcomes on knowledge propagation through network synchronization of dynamical models will be broadly applicable in other CPS domains such as smart grid. The educational component of this project will target training of CPS researchers and engineers on subjects in intelligent transportation and energy systems.

Over the past two decades, cities across the country have experienced a tremendous growth in cycling. As cities expand and improve their bicycle networks, local governments and bicycle associations are looking into ways of making cycling in urban areas safer. However, one of the main obstacles in decreasing the number of bicycle crashes is the lack of information regarding cycling safety at the street level. Historically, Bicycle Level of Service (BLOS) models have been used to measure street safety. Unfortunately, these models require extensive information about each particular roadway section, which often times is not available. This EArly-concept Grant for Exploratory Research (EAGER) project will provide innovative tools to automatically estimate street safety levels from crowd-sourced citizens' complaints as well as to shed some light into the traffic-related reasons behind such safety values. Ultimately, the outcomes of this project will contribute to the overall vision for Smart and Connected Communities (S&CC) by helping to reduce the number of crashes and human fatalities in the city using large streams of data collected from connected citizens. The project has strong support from multiple local institutions including Bike Share and local transportation departments. From a technical perspective, the main innovation will be the ability to automatically compute cycling safety measures using information extracted from citizen-generated complaints at very fine-grained spatio-temporal scales. For that purpose, the project will use data mining and machine-learning techniques to extract relevant quantitative and textual features from the crowd-sourced data. The expected outcomes of this project will be: (a) accurate and interpretable models to estimate street safety levels from user-generated data; (b) a set of easy-to-interpret, actionable items for local Departments of Transportation to improve cycling experiences and general safety; and (c) a dataset with user-generated complaints, cycling videos and safety levels per road segments to share with other researchers so as to advance the state of the art in data-driven cycling safety.

Wireless sensor-actuator networks (WSANs) are designed to collect and disseminate information using a physically distributed collection of wireless nodes and multi-hop protocols. WSANs are gaining rapid adoption in industrial automation and manufacturing applications due to their low deployment cost, robustness, and configuration flexibility. While the early success of industrial WSANs has been focused on monitoring applications, there are significant advantages, and also challenges, when WSANs are used in feedback control applications. Deployments of WSANs in control applications require careful design and testing of their network configurations and of the control algorithms employed, since undesired behaviors can arise from differences between expected and observed latency and reliability. This project creates a holistic approach to design and operate industrial wireless process control systems based on new closed-loop interactions between the controller of the industrial process and the WSAN manager. In this new closed loop, the controller, using a prediction model of the industrial process, estimates and compares the control performance loss associated to different network configurations. Accordingly, the WSAN manager estimates the quality of its internal links and adapts the network configuration to optimize the controller performance. At the core of this proposal is a holistic controller, which besides choosing the input signal for the physical process, selects a network configuration from a finite set of options with the goal of providing safety and performance guarantees. The holistic controller uses estimations of network status and physical process states to decide an appropriate network configuration, keeping in mind the inherent cost and delay associated to changes in scheduling and routing. The WSAN manager will, in turn, implement a new interface to communicate with the holistic controller, new mechanisms for efficient network reconfiguration, and new observers to estimate the probability of information delivery as a function of the different configurations and environmental conditions. The proposed feedback configuration between holistic controller and network manager enables industrial process control applications that are resilient against disturbances to both the physical plant and the wireless network. Furthermore, implementation in large-scale networks is enabled using a proposed bidirectional middleware, designed to transfer information between holistic controllers and network managers in real-time, exposing only the relevant features of each to the rest of the system. The results of this project will transform the way in which industrial wireless process control systems are designed, deployed, and operated, while establishing a new class of adaptive wireless control systems.

Wireless sensor-actuator networks (WSANs) are designed to collect and disseminate information using a physically distributed collection of wireless nodes and multi-hop protocols. WSANs are gaining rapid adoption in industrial automation and manufacturing applications due to their low deployment cost, robustness, and configuration flexibility. While the early success of industrial WSANs has been focused on monitoring applications, there are significant advantages, and also challenges, when WSANs are used in feedback control applications. Deployments of WSANs in control applications require careful design and testing of their network configurations and of the control algorithms employed, since undesired behaviors can arise from differences between expected and observed latency and reliability. This project creates a holistic approach to design and operate industrial wireless process control systems based on new closed-loop interactions between the controller of the industrial process and the WSAN manager. In this new closed loop, the controller, using a prediction model of the industrial process, estimates and compares the control performance loss associated to different network configurations. Accordingly, the WSAN manager estimates the quality of its internal links and adapts the network configuration to optimize the controller performance. At the core of this proposal is a holistic controller, which besides choosing the input signal for the physical process, selects a network configuration from a finite set of options with the goal of providing safety and performance guarantees. The holistic controller uses estimations of network status and physical process states to decide an appropriate network configuration, keeping in mind the inherent cost and delay associated to changes in scheduling and routing. The WSAN manager will, in turn, implement a new interface to communicate with the holistic controller, new mechanisms for efficient network reconfiguration, and new observers to estimate the probability of information delivery as a function of the different configurations and environmental conditions. The proposed feedback configuration between holistic controller and network manager enables industrial process control applications that are resilient against disturbances to both the physical plant and the wireless network. Furthermore, implementation in large-scale networks is enabled using a proposed bidirectional middleware, designed to transfer information between holistic controllers and network managers in real-time, exposing only the relevant features of each to the rest of the system. The results of this project will transform the way in which industrial wireless process control systems are designed, deployed, and operated, while establishing a new class of adaptive wireless control systems.

This NSF CPS EAGER project supports the SmartShuttle and SMOOTH II NIST GCTC technical cluster projects of the City of Columbus and the Ohio State University by developing a unified and scalable solution architecture for low speed automated shuttle deployment in a Smart City. This project will help the development of Columbus as a Smart City, having a broad impact on the mobility choices of its inhabitants. The targeted population of this project are the residents of the City of Columbus and more specifically the people who either work in or visit the Easton Town Center outdoor shopping center in Columbus. The proposed use of small electric shuttles as a first-mile, last-mile solution will improve their quality of life as a safe and reliable mobility solution. The results of the proof-of-concept demo will be used to quantify the potential societal impact as the number of people per day who will be able to use the proposed low speed automated shuttle solution in the Easton Town Center area and in other parts of Columbus in a potential future full scale deployment. The benefit to society of this project will be the development of a generic unified and scalable architecture for low speed automated driving shuttles that will be shared with GCTC teams and other interested researchers. Success of low speed automated shuttles in Smart Cities requires the use of a unified, scalable and replicable software, hardware, control and decision making architecture. This architecture should be easily adoptable and modifiable by different GCTC teams and other users and easily replicable for different deployment sites. This architecture should also be compliant with the automated driving architecture used by automotive OEMs in high speed automated driving. This project has three parts. The first part is the development of a unified software, hardware, control and decision making architecture that uses a model based design approach within the Simulink development environment consisting of generic Simulink interfaces for typical sensors like GPS, camera, lidar, radar and V2V modem, generic Simulink steering, throttle and brake actuators, all within a generic multi-agent Simulink automated driving architecture connected by generic and scalable control and decision making blocks. The second part is the development of a scalable and replicable method of designing longitudinal and lateral vehicle dynamics controllers using the parameter space approach to parametric robust control. The third part is an evaluation and rating system that will be developed to evaluate different automated driving control systems utilizing the unified and scalable architecture of the project. The results will be demonstrated using a proof-of-concept demo deployment in the Easton Town Center outdoor shopping area to solve first-mile and last-mile problems. Scalability and replicability of the proposed architecture will be demonstrated by application to a small two seater electric vehicle and a mid-sized hybrid electric sedan.

The project studies techniques for constructing guaranteed-safe control algorithms for maneuvering autonomous vehicles ("self-driving cars") under a variety of environmental conditions. Existing autonomous vehicles are able to navigate highways and surface streets reliably when the driving conditions do not pose significant challenges. However, future vehicles will need to handle pot-holes, snow, high winds, driving rain, darting animals, fog and all the other impediments that make driving in the real world challenging in the first place. Some of these conditions require "aggressive maneuvers" in the form of sudden acceleration, braking and/or rapid steering. Such aggressive maneuvers present significant challenges to existing autonomy algorithms, raising concerns regarding the safety of the passengers, other vehicles on the road and pedestrians. At the same time, guaranteeing safe behavior while in autonomous operation is critical for the adoption of these systems, and such guarantees demand the development of reliable and verified maneuvering. The Ninja Car platform at the University of Colorado, Boulder serves as an experimental platform for the verified algorithms, and is also used to educate students and enthusiasts on the design and implementation of autonomous vehicles. The research carried out in this project contributes to the ultimate vision of self-driving cars that are safe by focusing on guaranteed-safe algorithms for maneuvering. Furthermore, the educational activities seek to educate a new generation of students and enthusiasts from the general public on the design and deployment of self-driving cars. This project develops reliable control systems for maneuver regulation in autonomous ground vehicles that are adaptive to, and guaranteed for, a variety of driving conditions. The approach first considers the problem of developing a stack of increasingly complex models for autonomous vehicles. The simplest models serve to develop formally verified control algorithms for maneuver regulation and the corresponding set of maneuvers that can be carried out for varying road conditions. These results are transferred to more sophisticated models that use on-board sensors to fine-tune the control to the actual dynamics of the car (such as the wear on the shocks, tire pressure, etc.). Finally, building upon verified maneuvers for a single vehicle, the project studies cooperative maneuvers for multiple vehicles, wherein the vehicles communicate to meaningfully share information. The cooperating vehicles then implement verified collision avoidance schemes and share driving conditions (e.g. how slick a given road actually is) to formulate environment-aware, guaranteed-safe maneuvers. The research extends the growing body of work on applying formal methods for rigorously solving control problems. A framework of transverse control Lyapunov and barrier functions provides a basis for solving trajectory tracking problems for nonlinear dynamical systems. The work also investigates new constraint-solving approaches for synthesizing these functions for nonlinear systems. The research is evaluated using a 1/8th-scale model testbed called the Ninja Car at the University of Colorado, Boulder. The research ideas are also integrated into educational activities that use the Ninja Car as a cost effective system for instructing engineering students at all levels, and enthusiasts interested in autonomous vehicles, on the fundamental principles that underlie the design and deployment of these systems.

Since 2000, surgical robots have been used in over 1.75 million minimally invasive procedures in the U.S. across various surgical specialties, including gynecological, urological, general, cardiothoracic, and head and neck surgery. Robotic surgical procedures promise decreased complication rates and morbidity, due to the minimally invasive nature of the procedures. A detailed analysis (also reported to the FDA) of the adverse events associated with the surgical robot indicates that despite the increased number of robotic procedures and their greater utilization, the rate of adverse events has remained relatively steady over the last 14 years. Even though current surgical robots are designed with safety mechanisms in mind, in practice several significant challenges exist in enabling timely and accurate detection and mitigation of adverse incidents during surgery. Toward this goal, the project will address (i) an in-depth analysis of incident causes, which takes into account the interactions among the system components, human operators, and patients; (ii) resiliency assessment of the robotic systems in the presence of realistic safety hazards, reliability failures, and malicious tampering; and (iii) continuous monitoring for detection of safety, reliability, and security violations to ensure patient safety. The intellectual merit of this work lies in: (i) systems-theoretic approach driven by real data on safety hazards and medical equipment recalls, to identify causes leading to violation of safety constraints at different layers of the cyber and physical system-control-structure; (ii) creation of a unique safety hazard simulation engine to perform injections into robot control software and emulate realistic safety hazard scenarios in a virtual environment; (iii) an adaptive method for rapid detection of events that lead to safety violations, based on continuous monitoring of human operator actions, robot state, and patient status, in conjunction with a probabilistic graph-model that captures dependencies between the causal factors leading to safety hazards; and (iv) experimental validation using the real robot to assess monitoring and protection mechanisms in the presence of realistic safety hazards, reliability faults, and security exploits (recreated using safety hazard simulation engine). The broader impact of the project is a methodology for design and resiliency assessment of a larger class of control cyber-physical systems, which involve humans in the on-line decision making loop. Application of the methodology to robot-assisted surgery demonstrates the strength and practicality of the approach and is likely to attract interest from areas of academia and industry in which cyber-physical systems are either a subject of study or the basis for delivering a service (e.g., transportation or electric power grids). This project's educational outreach encompasses strategies for broadening participation in multi-disciplinary projects spanning medicine and engineering.