This project develops advanced cyber-physical sensing, modeling, control, and optimization methods to significantly improve the efficiency of algal biomass production using membrane bioreactor technologies for waste water processing and algal biofuel. Currently, many wastewater treatment plants are discharging treated wastewater containing significant amounts of nutrients, such as nitrogen, ammonium, and phosphate ions, directly into the water system, posing significant threats to the environment. Large-scale algae production represents one of the most promising and attractive solutions for simultaneous wastewater treatment and biofuel production. The critical bottleneck is low algae productivity and high biofuel production cost. The previous work of this research team has successfully developed an algae membrane bioreactor (A-MBR) technology for high-density algae production which doubles the productivity in an indoor bench-scale environment. The goal of this project is to explore advanced cyber-physical sensing, modeling, control, and optimization methods and co-design of the A-MBR system to bring the new algae production technology into the field. The specific goal is to increase the algal biomass productivity in current practice by three times in the field environment while minimizing land, capital, and operating costs. Specifically, the project will (1) adapt the A-MBR design to address unique new challenges for algae cultivation in field environments, (2) develop a multi-modality sensor network for real-time in-situ monitoring of key environmental variables for algae growth, (3) develop data-driven knowledge-based kinetic models for algae growth and automated methods for model calibration and verification using the real-time sensor network data, and (4) deploy the proposed CPS system and technologies in the field for performance evaluations and demonstrate its potentials. This project will demonstrate a new pathway toward green and sustainable algae cultivation and biofuel production using wastewater, addressing two important challenging issues faced by our nation and the world: wastewater treatment and renewable energy. It will provide unique and exciting opportunities for mentoring graduate students with interdisciplinary training opportunities, involving K-12 students, women and minority students. With web-based access and control, this project will convert the bench-scale and pilot scale algae cultivation systems into an exciting interactive online learning platform to educate undergraduate and high-school students about cyber-physical system design, process control, and renewable biofuel production.

Cyber-physical systems of the near future will collaborate with humans. Such cognitive systems will need to understand what the humans are doing. They will need to interpret human action in real-time and predict the humans' immediate intention in complex, noisy and cluttered environments. This proposal puts forward a new architecture for cognitive cyber-physical systems that can understand complex human activities, and focuses specifically on manipulation activities. The proposed architecture, motivated by biological perception and control, consists of three layers. At the bottom layer are vision processes that detect, recognize and track humans, their body parts, objects, tools, and object geometry. The middle layer contains symbolic models of the human activity, and it assembles through a grammatical description the recognized signal components of the previous layer into a representation of the ongoing activity. Finally, at the top layer is the cognitive control, which decides which parts of the scene will be processed next and which algorithms will be applied where. It modulates the vision processes by fetching additional knowledge when needed, and directs the attention by controlling the active vision system to direct its sensors to specific places. Thus, the bottom layer is the perception, the middle layer is the cognition, and the top layer is the control. All layers have access to a knowledge base, built in offline processes, which contains the semantics about the actions. The feasibility of the approach will be demonstrated through the development of a smart manufacturing system, called MONA LISA, which assists humans in assembly tasks. This system will monitor humans as they perform assembly task. It will recognize the assembly action and determine whether it is correct and will communicate to the human possible errors and suggest ways to proceed. The system will have advanced visual sensing and perception; action understanding grounded in robotics and human studies; semantic and procedural-like memory and reasoning, and a control module linking high-level reasoning and low-level perception for real time, reactive and proactive engagement with the human assembler. The proposed work will bring new tools and methodology to the areas of sensor networks and robotics and is applicable, besides smart manufacturing, to a large variety of sectors and applications. Being able to analyze human behavior using vision sensors will have impact on many sectors, ranging from healthcare and advanced driver assistance to human robot collaboration. The project will also catalyze K-12 outreach, new courseware (undergraduate and graduate), publication and open-source software.

Part 1: Upper-limb motor impairments arise from a wide range of clinical conditions including amputations, spinal cord injury, or stroke. Addressing lost hand function, therefore, is a major focus of rehabilitation interventions; and research in robotic hands and hand exoskeletons aimed at restoring fine motor control functions gained significant speed recently. Integration of these robots with neural control mechanisms is also an ongoing research direction. We will develop prosthetic and wearable hands controlled via nested control that seamlessly blends neural control based on human brain activity and dynamic control based on sensors on robots. These Hand Augmentation using Nested Decision (HAND) systems will also provide rudimentary tactile feedback to the user. The HAND design framework will contribute to the assistive and augmentative robotics field. The resulting technology will improve the quality of life for individuals with lost limb function. The project will help train engineers skilled in addressing multidisciplinary challenges. Through outreach activities, STEM careers will be promoted at the K-12 level, individuals from underrepresented groups in engineering will be recruited to engage in this research project, which will contribute to the diversity of the STEM workforce. Part 2: The team previously introduced the concept of human-in-the-loop cyber-physical systems (HILCPS). Using the HILCPS hardware-software co-design and automatic synthesis infrastructure, we will develop prosthetic and wearable HAND systems that are robust to uncertainty in human intent inference from physiological signals. One challenge arises from the fact that the human and the cyber system jointly operate on the same physical element. Synthesis of networked real-time applications from algorithm design environments poses a framework challenge. These will be addressed by a tightly coupled optimal nested control strategy that relies on EEG-EMG-context fusion for human intent inference. Custom distributed embedded computational and robotic platforms will be built and iteratively refined. This work will enhance the HILCPS design framework, while simultaneously making novel contributions to body/brain interface technology and assistive/augmentative robot technology. Specifically we will (1) develop a theoretical EEG-EMG-context fusion framework for agile HILCPS application domains; (2) develop theory for and design novel control theoretic solutions to handle uncertainty, blend motion/force planning with high-level human intent and ambient intelligence to robustly execute daily manipulation activities; (3) further develop and refine the HILCPS domain-specific design framework to enable rapid deployment of HILCPS algorithms onto distributed embedded systems, empowering a new class of real-time algorithms that achieve distributed embedded sensing, analysis, and decision making; (4) develop new paradigms to replace, retrain or augment hand function via the prosthetic/wearable HAND by optimizing performance on a subject-by-subject basis.

This proposal addresses a multidisciplinary workshop with academic researchers, corporate technology providers, and agricultural producers to define research challenges and a research road-map to address the following major FEWS challenges: 1. Developing novel targeted remote sensing and in-situ sensing technology that can be practically fielded and used in food and water system management. 2. Developing novel integrated hydrology, soil, microclimate, and plant/agricultural production models that interact accurately and across traditional scales for understanding local, regional, and national impacts. 3. Turning this developing and pending FEWS data deluge into usable, actionable information for agricultural producers, local and regional decision makers, and citizens. The workshop addresses the emerging issues in the food/water/energy system throughout the diverse geography of United States and over various crops and environmental conditions to better understand and model, and ultimate devise a solution for the changes to the FEWS system. The solution must be multifaceted, multidisciplinary in order to incorporate sensing, hydrology, visual analytics, and the potential for increased climate change. The workshop will generate a report and other artifacts that will lead to research into solving these challenges and have an impact on scientific fields including, sensing technology, hydrology, soil science, climate, data fusion, analysis, visualization, and data driven decision making, as well as agricultural production, local and regional economies, sustainability and planning.

Laboratory-on-a-chip (LoC) technology is poised to improve global health through development of low-cost, automated point-of-care testing devices. In countries with few healthcare resources, clinics often have drugs to treat an illness, but lack diagnostic tools to identify patients who need them. To enable low-cost diagnostics with minimal laboratory support, this project will investigate domain-specific LoC programming language and compiler design in conjunction with device fabrication technologies (process flows, sensor integration, etc.). The project will culminate by building a working LoC that controls fluid motion through electronic signals supplied by a host PC; a forensic toxicology immunoassay will be programmed in software and executed on the device. This experiment will demonstrate benefits of programmable LoC technology including miniaturization (reduced reagent consumption), automation (reduced costs and uncertainties associated with human interaction), and general-purpose software-programmability (the device can execute a wide variety of biochemical reactions, all specified in software). Information necessary to reproduce the device, along with all software artifacts developed through this research effort, will be publicly disseminated. This will promote widespread usage of software-programmable LoC technology among researchers in the biological sciences, along with public and industrial sectors including healthcare and public health, biotechnology, water supply management, environmental toxicity monitoring, and many others. This project designs and implements a software-programmable cyber-physical laboratory-on-a-chip (LoC) that can execute a wide variety of biological protocols. By integrating sensors during fabrication, the LoC obtains the capability to send feedback in real-time to the PC controller, which can then make intelligent decisions regarding which biological operations to execute next. To bring this innovative and transformative platform to fruition, the project tackles several formidable research challenges: (1) cyber-physical LoC programming models and compiler design; (2) LoC fabrication, including process flows and cyber-physical sensor integration; and (3) LoC applications that rely on cyber-physical sensory feedback and real-time decision-making. By constructing a working prototype LoC, and programming a representative feedback-driven forensic toxicology immunoassay, the project demonstrates that the proposed system can automatically execute biochemical reactions that require a closed feedback loop. Expected broader impacts of the proposed work include reduced cost and increased reliability of clinical diagnostics, engagement with U.S. companies that use LoC technology, training of graduate and undergraduate students, increased engagement and retention efforts targeting women and underrepresented minorities, student-facilitated peer-instruction at UC Riverside, a summer residential program for underrepresented minority high-school students at the University of Tennessee, collaborations with researchers at the Oak Ridge National Laboratory, and creation, presentation, and dissemination of tutorial materials to promote the adoption and use of software-programmable LoCs among the wider scientific community.