CITRIS is pleased to announce that 11 innovative proposals have been selected in this year’s round of seed funding. Each of these proposals is multi-campus with lead investigators from numerous departments. We received a total of 44 proposals for using information technology in the service of society.
“The seed funding process lets us connect with researchers embarking on pioneering projects that have the possibility of an impact on society and could lead to future funding on the CITRIS campuses,” notes CITRIS Director Costas Spanos. “Bringing together groups from diverse fields can yield unique solutions to societal challenges.”
Thank you to all who submitted proposals. We look forward to the discoveries and developments that will arise as a result of this round of funding.
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- Quantifying the Value of Hydrologic Forecasting for Intelligent Hydropower Operations. Jay Lund, UC Davis, and Roger Bales, UC Merced. This project will develop tools to measure the economic benefits of improved hydrological forecasting using monitoring networks. The work will be scalable to managed water systems and improve our ability to manage intelligent water infrastructure.
- Environmental DNA (eDNA) Smart Sampling Using Unmanned Aerial Vehicles (UAV). YangQuan Chen, UC Merced, and Michael Miller, UC Davis. eDNA (environmental DNA, which is shed by all organisms into their environment) can be isolated from water samples and analyzed to detect the presence of aquatic organisms. This project will develop and test a ruggedized water sample drone that can take off and land on the surface of running water under windy conditions with smart sensing strategies and then collect water samples to analyze and report.
- Campus Building Web Services: A Case Study on Campus Level Deployment of sMAP. Alberto Cerpa, UC Merced and David Culler, UC Berkeley. This project will use the sMAP open source project (the Simple Measurement and Actuation Profile) to make data from the UC Merced campus available and usable to the research community. The buildings use sensors to gather data that can be used to general applications for temperature control and comfort based on individual occupancy monitoring and user feedback.
- Model Predictive Control of PV-ES System utilizing Second Life Lithium Battery. Jae Wan Park, UC Davis, and Scott Moura, UC Berkeley. This project will focus on recycling batteries that have been retired from plug-in electric vehicles as part of a smart energy storage system for single-family homes. The system uses generated solar energy from the day and reduces a portion of the energy use at night by using the stored solar energy that resides in the battery.
- Bedside to the Cloud and Back: Real-Time Data Analytics from Critical Care Instrumentation. Nicholas Anderson, UC Davis; Jason Adams, UC Davis Medical Center; and Brian Barsky, UC Berkeley. This project will develop a system-based workflow to securely acquire wireless data from mechanical ventilators in critical care environments, and leverage scalable web-based analytic platforms to advance data analytics and visualization of issues surrounding patients with respiratory failure. This project may lead to generalizable data analytic frameworks for high-volume instrumentation patient data.
- Optofluidic Platform for Single Cell Genomics. Ming Wu, UC Berkeley, and Nader Pourmand, UC Santa Cruz. A major barrier to successful cancer treatment is the recurrence of cancer cells with acquired resistance to chemotherapy. Recent work by this group has used single-cell sequencing to identify gene expression at the single-cell level in an insightful drug-tolerance experiment. This project will bring together the gene research with that of novel optoelectronic tweezers that can sort and isolate single cells in a massively parallel manner to develop an optofluidic platform for high throughput analysis.
- Non-invasive Multi-sensor to Predict Worsening Heart Failure. Matthew Guthaus, UC Santa Cruz; Kathleen Tong, UC Davis Medical Center; and Liviu Klein, UCSF Medical Center. This project will study a low-cost bio-sensing system to non-invasively predict heart failure in a home setting. The device will be able to measure physiological signals and characterize heart health. This sensor could allow for a more rapid treatment protocol and potentially decrease the need for hospital admissions for heart failure.
- Optimal Design of Smart Urban Crowd-Sensing. Alexey Pozdnukhov, UC Berkeley and Shawn Newsam, UC Merced. The recent technology trends of crowd-sourcing/sensing and location-based social networking have reignited citizen engagement, opening new perspectives for cost-effective ways of making local communities and cities more sustainable. This project will develop optimal strategies for citizen involvement in monitoring urban space, reporting issues and improving accountability of a city’s response by increasing the usability of multi-media data collection through automated context recognition and optimally designing crowd-sensing campaigns in location-based social networks.
- Stories of Solidarity. Jesse Drew & Glenda Drew, UC Davis; Ken Jacobs, UC Berkeley; Robin Delugan, UC Merced. This project is an innovative social media platform that draws upon the humanities, arts, social sciences, and computer sciences to address the enormous problems faced by millions of low-wage workers today. The project includes storytelling, solidarity-building, and advocacy, allowing workers to share their stories of precarious employment in order to develop new strategies for economic justice.
- Participatory Mapping to Inform Agricultural Decision Making. Tapan Parikh and Adam Calo, UC Berkeley, and Deb Niemeier, UC Davis. This research aims to develop and pilot mapping tools that enable small farmers to influence local agricultural policy. Using crowd-sourced data, mapping, and visualization tools, farmers enrolled in certain programs will lead a process of inquiry around pressing agricultural policies, including water runoff monitoring requirements, the newly imposed Food Safety Modernization Act, and prescribed burning restrictions on US Forest Service lands.
- Integrated Electrostatic Precipitators and Sensors with Engineered Semiconductor Nanotips for Monitoring and Controlling Pollution. M. Saif Islam, UC Davis and Ming Wu, UC Berkeley. This project will demonstrate micro electrostatic precipitators for pollution monitoring and control of future intelligent infrastructures. The pollutant particles are charged and collected on oppositely charge plates, but current systems that operate in power plants and manufacturing plants are bulky and run at high voltage. By developing microdevices used ultra-sharp semiconductor nanowires as the ionization centers, the voltage and size will be drastically decreased.