This Small Business Innovation Research (SBIR) Phase I project will develop new technology for the separation of ultra-fast-rise-time incident and reflected step functions needed for the development of an integrated differential Time Domain Reflectometer (TDR) to be used in soil water content measurement. Because of residual reflections from circuit board striplines terminated in variable soil loads and reflections from wetting fronts and discontinuities in the soil, the derivation of propagation time, permittivity and water content are subject to errors caused by the vector addition of the spurious, shorter-term reflections with the main reflection from the end of the waveguide. The characteristics of the interfering spurious signals vary with the soil environment. The overall waveform appearing at the TDR digitizer is a composite image of an incident step, a reflected wave and the set of spurious signals. The research required is that of characterizing the spurious signal patterns and of developing machine learning and signal processing algorithms to remove their impact from the derivation of reflected wave propagation time. Identification and timing of wetting fronts will also be included to provide sensor users with infiltration monitoring capability. <br/><br/>The broader impact/commercial potential of this project is to provide an affordable and easily deployed means of accurately measuring soil water content, including the credible measurement of water uptake by food crops. This capability will facilitate significant increases in water use efficiency - the foundation for growing high quality food with less water. The value proposition to crop growers is reduced marginal costs and increased crop yield and quality, with a potential payback time of one to two growing seasons. Commercial adoption of soil moisture sensing technology will help manage the increased cost and demand for fresh water, of which over 70% of consumption goes to agriculture. The technology will also provide a stable and credible measurement tool for studying and monitoring watershed dynamics for flood and drought prediction, for monitoring dams, mudslide areas and levees for potential failure and for studying the impact of soil water dynamics on weather. Current tools for these measurements are either heavily regulated (neutron probe), difficult to install (Time Domain Transmissometer probe) or provide unstable readings with changing soil electrical conductivity and compaction (capacitive probes).
This Small Business Innovation Research (SBIR) Phase I project fills a key technological gap in development of biological and chemical sensor development. There is an unmet need for tests that easily detect molecular, cellular, or clinical responses that indicate disease onset or predict which candidate drugs will have harmful side effects. In this project, we are addressing this need by innovating and commercializing a reliable, sensitive, and accurate test that simultaneously monitors and quantifies levels of multiple proteins in biological fluids (plasma, urine, serum). These target protein biomarkers indicate disease onset, disease progression, or drug efficacy. The test is simple and can be performed using existing laboratory equipment, allowing for easy adoption in academic labs and pharmaceutical companies. Furthermore, this test is the only test that reliably prevents false positive signals, which typically result from nonspecific binding between non-target protein-detection agents. Preventing false positive signals is crucial because it enables more accurate measure of proteins in biological fluids. An initial test panel of proteins to be tested will be for cardiovascular disease management and drug development.<br/><br/>The broader impact/commercial application of this project is in the $1 billion life science research market that today is served mainly by tests that can only detect a single protein at a time. Measuring proteins one by one is time- and labor-intensive. The proposed research has tremendous potential as a research tool for highly accurate detection and analyses of protein biomarkers, leading to better understanding of disease pathogenesis and therapeutic response. The innovative, new test also has potential applications in the $36.6 billion contract research organization market to improve drug safety and efficacy testing, decrease clinical trial costs, and decrease time to market. This proposal advances the fields of Biological and Chemical Technologies (BC) as well as of Nanotechnology, Advanced Materials, and Manufacturing (NM)
The research team proposed to develop a tool for field geologists, golf-mapping industry and ecological monitoring, in which topography is mapped using cameras mounted on an auto-piloted Kite Unmanned Aerial Vehicles (UAVs). These cameras could be visual, near-infrared or thermal or a combination of all of them. The system can generate high resolution (< 1 m) textured Digital Elevation Models (DEMs) that can be uploaded into Google Earth along with derived products such as hillshade and slope maps, providing user-friendly ways of monitoring and mapping. While there are currently a number of UAV technologies and 3D mapping products, this solution has the following features 1) Use of a cost-effective Kite platform that can be flown without any training 2) Multiple sensor integration capability i.e., integrating visual cameras with near-infrared and thermal cameras for enabling novel applications in the field of agricultural monitoring, ecological mapping and pipeline mapping 3) Integration with Google Earth and Maps for easy and effective planning and visualization. The tools that will be developed as part of the proposal will be intuitive and end-user oriented. This will enable UAVs to be used effectively for civilian purposes by non-air-qualified personnel.<br/><br/>Cost-effective Unmanned Aerial Vehicles could change the way large areas are monitored: from crop monitoring to cattle monitoring to environmental protection. The tools that are developed as part of this project may enable these UAVs to be used effectively for civilian purposes. This could usher in a new era of inexpensive, cost-effective vehicles that can be used by farmers, agribusinesses, and mapping professionals, providing businesses with an unprecedented way of increasing the quality, reliability, and amount of information available to them.
Following an earthquake, or similar natural disaster, a key problem is rapid and accurate on-site damage assessment to support local first responders; however, trained experts are typically remote from the disaster and it can be time consuming and expensive to bring them onsite. Accessing remote experts to improve the accuracy of rapid assessments is a promising method to streamline provisioning of emergency shelters and other resources. This project focuses on new methods for improved rapid assessment of earthquake damaged building structures in Christchurch, New Zealand. The methods are based on collaboration using augmented-reality (AR) imagery, mobile phone based sensor technologies and crowdsourcing techniques for guided remote data collection. A key element of the system is intuitive remote collaboration. Our mobile AR system can be used to connect a user in the disaster zone to a remote expert via audio and shared still images and/or video, helping them to rapidly collect data on building structural integrity. A user evaluation will be performed to compare the performance between the prototype and more traditional approaches (e.g., waiting for an expert to arrive on the ground), and assessment based on imagery recorded from an untrained and unguided user. Two hypotheses will be tested: 1) a collaborative mobile AR system can improve the quality and type of data collected for structural assessment 2) the time to provide data from non-experts assisted by experts to decision makers in a digestible format is dramatically reduced as compared to traditional methods.<br/><br/>The approach will enable rapid post-event damage assessment, streamline emergency provisioning of shelters by allowing people to stay in safe dwellings, and speed up emergency response and reconstruction. The resulting valuable dataset will assist development of rapid assessment forms, contribute to earthquake structural damage case studies, provide key baseline to test several computer science research projects on improved disaster response, and provide key data for development of life-saving tools. The international collaboration also provides engagement of underrepresented groups in this computing research.
Despite their increasingly ubiquitous deployment, RFID systems are plagued with a wide variety of security and privacy threats. A large number of these threats arise due to the tag?s promiscuous response to any reader requests. This renders sensitive tag information easily subject to unauthorized reading. It also incites different forms of relay attacks whereby a colluding pair, by relaying information between a legitimate tag and reader, can successfully impersonate the legitimate tag without actually possessing it.<br/><br/>This research explores novel context-aware security and privacy mechanisms by leveraging the newly-equipped sensing capabilities on the next generation (passive) RFID tags. The goal is to provide improved protection against unauthorized reading and relay attacks without undermining the usability and efficiency offered by the RFID systems. The project also includes a feasibility study of the proposed mechanisms in terms of both economical and power constraints, and a systematic analysis of possible (new) sensor-centric attacks. The overall proposed activities range from system design and analysis to implementation and performance measurements. <br/><br/>More broadly, this exploratory work intends to arrive at a better understanding of the feasibility of utilizing parameters derived from the physical world to solve security and privacy issues of the cyber systems. In terms of educational activities, new security courses focusing on resource-constrained devices and lightweight cryptographic tools will be developed.
This SBIR Phase I research proposal proposes research to develop an ultra-compact, low cost<br/>system for wirelessly monitoring motors that will cost manufacturers less than $300/ motor<br/>to implement. The research will leverage existing patent-pending energy-efficient<br/>algorithms for determining motor condition based on vibration and temperature data to develop<br/>wireless nodes capable of autonomously determining the condition of any motor to which they<br/>are attached. The research will result in a prototype system of wireless nodes implemented<br/>at an industrial partner, which will provide the necessary incentive for future investment<br/>in the company and technology.<br/><br/>The broader impact of this research will be to enable a wireless system to facilitate<br/>condition-based maintenance of electric motors in industrial facilities at a cost of less<br/>than $300 per motor to manufacturers, which is about 10% of the cost of current systems.<br/>At this price point, tens of thousands of facilities around the United States will be able<br/>to afford the initial investment to implement condition-based maintenance on their motor systems.<br/>Since condition-based maintenance has been shown to maximize up-time and minimize yearly maintenance<br/>costs, this will increase the competitiveness of American manufacturing and ultimately<br/>help create more manufacturing sector jobs. Additionally, the prototype system produced as<br/>a result of the research will provide an important proof-of-concept for low-cost, low-power<br/>wireless sensor nodes that should help spur future development and investment in this field,<br/>which is in turn instrumental for the development of "smart grids", "smart cities", and other<br/>intelligent infrastructure.
This funding renews a long-running and highly-successful CISE Research<br/>Experiences for Undergraduates (REU) site at DePauw University. The<br/>site exposes students to a range of topics in Computer Science, including<br/>pen-based computing, computer-supported cooperative work, assistive<br/>technology, natural language processing, functional programming<br/>languages, wireless sensor networks, programming pedagogy, parallel<br/>programming, and virtual reality. Students will work in small teams<br/>with a mentor and will experience conducting research as part of a team,<br/>disseminating research results, and participating in a community of<br/>scholars.<br/><br/>This REU site will allow 24 students (over three years) to experience<br/>research in a variety of areas in Computer Science. The site focuses<br/>on recruiting students from undergraduate-only institutions, who do not<br/>have as ready access to research opportunities as students at research<br/>universities. The site?s primary objective is to encourage talented<br/>students enrolled at undergraduate institutions to pursue graduate<br/>studies and research careers in Computer Science.
The goal is to understand how plants sense their nutritional status with respect to the macronutrient sulfur (S). Plants are able to adjust their growth and physiology in response to the level of mineral nutrients available to them in soil. When nutrient level is low they become more efficient at nutrient utilization. Very little is known about how plants sense nutrients or even what exactly is sensed. The current project could set the stage for improving nutrient use efficiency thereby reducing the input costs and environmental impact associated with the use of fertilizers in agriculture.<br/><br/>A forward genetic approach has been used to identify a gene that is involved in S-sensing in the model plant Arabidopsis thaliana. The project will use both genetic and molecular approaches to understand how the sensor functions. What is learned will be a breakthrough not only for the field of plant S-nutrition, but also for the larger field of nutrient receptors. The results from these activities will be disseminated at symposia, conferences and in journal publications. The genetic resources generated by the project will be made available to the research community.<br/><br/>This is a collaborative project between two institutions, Lehman College of the City University of New York, a federally designated Hispanic Institution, and Rutgers University. The project will recruit minority undergraduate students and high school teachers to participate in research at both institutions through an active exchange program. The aim is to use a provocative research project to stimulate the interest of students from minority groups to pursue careers in plant biology and to enhance the US competitiveness in science.