Current Projects

Research

APM EAGER: WHEAT PHENOME/GENOME SENSING/MODELING VIA MICROWAVE SCATTERING INVERSION
Lead Institution: Kansas State University
Carlton Leuschen (KU –PI, Fernando Rodriguez-Morales, Co-I)
Sponsor: USDA/National Institute of Food and Agriculture
12/01/2016-11/30/2019

Wheat is a globally important grain at the forefront of food security issues. But like rice, maize, and sorghum, wheat is achieving barely 50% of the annual yield progress rate necessary to meet the food needs widely forecast for 2050. For over ten years, researchers have been engaged in developing an approach melding ecophysiological and quantitative genetic modeling with the potential to accelerate breeding rates of gain. This entails a two-step process that first fits crop models to data and then association maps the resulting parameter values to genetic markers. However, technological limits impede collection of the large amounts of needed plant trait data, especially the geometry of dense plant canopies. Targeting Kansas wheat breeding trials, this project is a proof-of-concept test combining microwave radar sensing with a novel inversion algorithm to ameliorate the situation.
The basic rationale is that:

(1) it is unnecessary to sense the 3D position, angle, and size of every tiller and leaf in a trial plot - rather one desires the genetic markers and effect sizes associated with these quantities' statistical distributions;

(2) models interrelating markers and morphology exist;

(3) if radar calculations for plant canopies can be accelerated, then the models in (2) can be inverted to yield genetics in a single-step; and

(4) an extension of the Analytical Element Method (AEM) from hydrology to electromagnetic (EM) wave propagation can provide such a speed up.

Briefly, the AEM exactly solves the field equations for very simple shapes that are then combined to yield machine accurate-answers for complex geometries. Unlike solvers in common use, the AEM only calculates solutions at the specific points of interest, thus hugely reducing computational loads. Prior work has found AEM solutions for EM waves in two dimensions. This project will extend those solutions to full 3D.

Concurrently, an existing wheat model that predicts highly realistic plant shapes will be modified so its outputs are expressed in terms of the AEM basic shapes. A three-layer model will then be built comprising [genetic markers : plant shapes : EM fields] and solved by probabilistic methods. This will yield the genetic markers most associated with the plant shapes sensed by radar. The method will be tested by team members with radar expertise using the facilities of the Center for Remote Sensing of Ice Sheets. Experiments in a large anechoic chamber will compare AEM predictions to actual radar reflectance data for simplified targets. The EM properties of wheat at radar frequencies will also be measured in the chamber using small, movable plots.Based on these data, a prototype field system will be constructed and used to gather plot data in a field trial conducted as part of the on-going Kansas wheat breeding program. Two tests will be performed. First the radar data will be association mapped directly to detect any responses to genetically determined canopy features. If positive results are found, they will be compared to published phenotypic mapping studies and hypotheses developed as to features to which the radar might be responding. The second test will solve the three layer model described above and also compare the results to existing literature.

ACTIVE WING SHAPING CONTROL FOR MORPHING AIRCRAFT
Lead Institution: Wichita State University
KU PI: Shawn Keshmiri
Sponsor: NASA EPSCOR
08/01/2015-07/31/2019

The objective of this proposal is the design, development and testing of certifiable control laws for active wing shaping of the VCCTEF (Variable Camber Continuous Trailing Edge Flap) aircraft. This aircraft has been conceptualize by NASA and is a high priority for the NASA ARMD Fixed Wing Project. Design of control laws for this aircraft is still very much an open topic since the use of active wing shaping control, in addition to traditional flight path control, is required in order to achieve the enhanced performance (in terms of higher lift-to-drag ratio) that the VCCTEF is capable of generating.

The wing shaping control increases the complexity of the flight control laws as it must make use of active sensing and feedback of wing shape to continuously modulate the camber across multiple sections of the wings so as to ensure that the local angle of attack distribution over the wing is optimal for every flight condition.

The research tasks outlined in this proposal are:

(1) Development of certifiable adaptive decentralized control laws for active wing shaping,

(2) Optimal number and placement of sensors on the wing to measure the wing shape,

(3) Distributed sensing system for real-time wing shape monitoring and feedback of the wing shape to the control laws, and

(4) Development of a testbed morphing UAV for testing the wing shaping control laws in the wind tunnel and in flight.

Morphing and the use of low-weight elastic aircraft is enabling technology that has the potential to lead to reduced aircraft drag and thus significant reduction in aircraft fuel consumption. This has a direct societal benefit since it leads to potentially cheaper and more affordable air transportation, and has a positive impact on the environment. In addition to this, the broader impacts of the proposed research include:

(1) Making the general aviation community aware of the performance benefits of morphing aircraft,

(2) Disseminating NASA morphing aircraft research into the general aviation community,

(3) Giving the KS and MO aviation industry a global leading role in using morphing control technology on civil aviation aircraft, and

(4) Contributing to the development of a technologically advanced workforce by educating KS and MO students in advanced nonlinear robust flight control systems and morphing aircraft.

OIB AIRBORNE RADAR SURVEYS OF LAND AND SEA ICE AND DATA PROCESSING USING CRESIS INSTRUMENTATION TO SUPPORT ICEBRIDGE OBSERVATIONS
Lead Institution: KU
PI: Carl Leuschen Co-Is: Emily Arnold, Rick Hale, John Paden and Fernando Rodriguez-Morales
Sponsor: NASA

To support the third phase of NASA’s Operation IceBridge (OIB) mission, we propose to perform airborne radar measurements on five OIB deployments, generate data products that provide detailed information on the three-dimensional structure of land and sea ice, and submit data products to the National Snow and Ice Data Center (NSIDC). The Center for Remote Sensing of Ice Sheets (CReSIS) has contributed to the IceBridge mission since 2009 by performing radar measurements on fixed wing aircraft and generating data products. The proposed instrumentation and resulting data products directly address critical and desirable measurements identified in the NASA ROSES 2015 IceBridge Observations research announcement.

We will perform radar mapping of the following elements:

(1) the beds of land-based ice sheets (critical & desirable);

(2) snow on sea ice (critical) and land ice (desirable); and

(3) ice structures (desirable).

The CReSIS radars also support altimetry (critical) by providing very high-resolution measurements of snow cover at Ku-band frequencies, which is important to the interpretation of measurements from the SAR Interferometric Radar Altimeter (SIRAL) on-board CryoSAT-2. As an addition, we propose the application of tomographic and other three-dimensional processing algorithms to radar depth-sounder/imager data for the production of high-resolution bed topography maps.

LEARNING AUTOPILOT SYSTEM FOR UNMANNED AERIAL SYSTEMS

Lead Institution: DarCorp
KU PI- Shawn Keshmiri
Sponsor: NASA SBIR

The project consists of the development of a new intelligent flight control system with learning capabilities and a high degree of assurance which is eligible for FAA certification. Machine learning and artificial intelligent research has led to many recent developments in cognitive control and decision making. Although automatic flight controllers are widely used and they have become common in recent years, they often lack intelligence, adaptability, and high performance. The reliability of UASs in unforeseen conditions is a direct function of their intelligence and adaptability.

The proposed project aims to take advantage of high-performance computing platforms and the state-of-the art machine learning and verification algorithms to develop a new intelligent, adaptable, and certifiable flight control system with learning capabilities. The autopilot system will be able to learn from each flight experience and develop intuition to adapt to a high level of uncertainties. To provide a high degree of assurance and to make the learning autopilot system safe and certifiable, a secondary and conventional autopilot system will be integrated based on the run-time assurance architecture. A monitor will be developed to continuously check aircraft states, envelope protection limits, and hand over aircraft control to the conventional autopilot system if needed. Provable guarantees of the monitor and the controllers will be provided using formal analysis. The propose a hybrid flight control system which has adaptability and intelligence of skilled pilots and at the same is cable of performing complex analysis and decision making algorithms in real-time.  We aim to build and train an artificial neural network model that can mimic the performance of the classical robust optimal controllers, extend the robustness, adaptability, and curiosity of the artificial neural network controller and integrate a Real-Time Assurance (RTA) system.

CIF21 DIBBs: MIDDLEWARE AND HIGH PERFORMANCE ANALYTICS LIBRARIES FOR SCALABLE DATA SCIENCE
Lead Institution: Indiana University
KU PI: John Paden
Sponsor: NSF

Many scientific problems depend on the ability to analyze and compute on large amounts of data. This analysis often does not scale well; its effectiveness is hampered by the increasing volume, variety and rate of change (velocity) of big data. This project will design, develop and implement building blocks that enable a fundamental improvement in the ability to support data intensive analysis on a broad range of cyberinfrastructure, including that supported by NSF for the scientific community. The project will integrate features of traditional high-performance computing, such as scientific libraries, communication and resource management middleware, with the rich set of capabilities found in the commercial Big Data ecosystem. The latter includes many important software systems such as Hadoop, available from the Apache open source community. A collaboration between university teams at Arizona, Emory, Indiana (lead), Kansas, Rutgers, Virginia Tech, and Utah provides the broad expertise needed to design and successfully execute the project. The project will engage scientists and educators with annual workshops and activities at discipline-specific meetings, both to gather requirements for and feedback on its software. It will include under-represented communities with summer experiences, and will develop curriculum modules that include demonstrations built as 'Data Analytics as a Service.'

The project will design and implement a software Middleware for Data-Intensive Analytics and Science (MIDAS) that will enable scalable applications with the performance of HPC (High Performance Computing) and the rich functionality of the commodity Apache Big Data Stack. Further, this project will design and implement a set of cross-cutting high-performance data-analysis libraries; SPIDAL (Scalable Parallel Interoperable Data Analytics Library) will support new programming and execution models for data-intensive analysis in a wide range of science and engineering applications. The project addresses major data challenges in seven different communities: Biomolecular Simulations, Network and Computational Social Science, Epidemiology, Computer Vision, Spatial Geographical Information Systems, Remote Sensing for Polar Science, and Pathology Informatics. The project libraries will have the same beneficial impact on data analytics that scientific libraries such as PETSc, MPI and ScaLAPACK have had for supercomputer simulations. These libraries will be implemented to be scalable and interoperable across a range of computing systems including clouds, clusters and supercomputers.

CONTROLS ON ICEBERG DISTRIBUTION AROUND GREENLAND
Sponsor: NASA
PI: Leigh Stearns

5/9/2016 - 5/8/2019

NSF-NERC: GROUND GEOPHYSICS SURVEY OF THWAITES GLACIER

Lead Institution: Pennsylvania State University
KU-PI: Leigh Stearns, Co-I: Carl Leuschen, Jilu Li
03/16-2018-02/28/2023

This project contributes to the joint initiative launched by the U.S. National Science Foundation (NSF) and the U.K. Natural Environment Research Council (NERC) to substantially improve decadal and longer-term projections of ice loss and sea-level rise originating from Thwaites Glacier in West Antarctica. Global costs of rapid sea-level rise to infrastructure (e.g., houses, roads, farms, ports) are likely to be large. A possible source of water for sea-level rise is the West Antarctic Ice Sheet, and Thwaites Glacier in particular. Ice sheets and glaciers contain vast quantities of water (in the form of ice) that is continually shed to the ocean, and continually replenished by snowfall (from water that evaporates from the oceans). If the amount of ice that Thwaites Glacier loses to the ocean over the next decades is much greater than the amount it receives as snowfall, then sea level in all the world's oceans would rise, possibly as much as a meter (approximately 3 feet). In order to estimate how likely such a catastrophic scenario would be, we need to better understand the surface over which Thwaites Glacier slides. If we can better characterize that layer (ask "is it smooth? Is it rough? Is it soft? Is it hard?"), then computer models of Thwaites would be much improved for better projections of the amount of ice that Thwaites Glacier would shed to the ocean.

The objectives of the project are to learn whether basal conditions allow for rapid retreat of the Thwaites Glacier grounding line or whether retreat may re-stabilize near its current grounding line. These objectives will be achieved by using dedicated ice-flow modeling to guide targeted field surveys and experiments over two seasons, and to measure the most important unknown quantities and incorporate them into the models. Numerical models will be used to generate hypotheses for basal conditions that are testable through geophysical surveys and to project future behavior of Thwaites Glacier after assimilating the resulting data. The geophysical methods include seismic, radar, gravity, and electrical surveys that together will allow for a fuller characterization of the bed. We will conduct field surveys in areas representative of different parts of the glacier, including across the margins, near the grounding line, and along the central axis of the glacier into its catchment.

A WIDEBAND RADAR ICE SOUNDER AND ULTRA-WIDEBAND RADAR FOR SURFACE-BASED MEASUREMENTS IN EAST ANTARCTICA

PI: David Braaten

Sponsors: JPL, NPI

MELTING AT THWAITES GROUNDING ZONE AND ITS CONTROL ON SEA LEVEL (THAWITES-MELT)

KU PI: John Paden

Sponsors: National Science Foundation

04/01/2018-03/31/2018

This project contributes to the joint initiative launched by the U.S. National Science Foundation (NSF) and the U.K. Natural Environment Research Council (NERC) to substantially improve decadal and longer-term projections of ice loss and sea-level rise originating from Thwaites Glacier in West Antarctica. The fate of the West Antarctic Ice Sheet (WAIS) is one of the largest uncertainties in projections of sea-level change. Thwaites Glacier (TG) is a primary contributor to sea-level rise and its flow is accelerating. This faster flow is a response to reduced buttressing from its thinning, floating ice shelf, and is ultimately caused by ocean-driven melting. The degree to which costly and geopolitically-challenging sea-level rise will occur therefore hangs to a large extent on ice-ocean interactions beneath such Antarctic ice shelves. However, the Thwaites system is not sufficiently well understood, exposing a significant gap in our understanding of WAIS retreat, its ocean-driven forcing, and the consequences for sea level. The chief regulators of TG's retreat are ice and ocean processes in its grounding zone, the location where the ice flowing from inland goes afloat. Ice and ocean processes at this precise locale are central to our understanding of sea-level rise, yet key variables have not been constrained by observation. Model projections of TG's future display extreme sensitivity to melting in the grounding zone and how that melting is applied. Equally-credible melt rates and grounding-zone glaciological treatments yield divergent trajectories for the future of West Antarctica, ranging from little change to large-scale ice sheet collapse with a half a meter or more of sea-level rise. The enormous uncertainty in outcome stems from the lack of observations in this critical grounding zone region. The enhanced understanding of melting of TG's ice shelf that will come from this project's focused observational program will be built into state-of-the-art coupled ice-sheet and ocean models. These physics-rich, high-resolution models will allow the potential sea-level contribution of TG to be bounded to an unprecedented degree.

This project will enable global and regional climate modelers to make a substantial improvement to projections of future ocean conditions over the continental shelf by providing physics-based projections of TG's sea-level contribution. The team proposes a suite of integrated activities: (1) multi-year oceanographic time series from beneath TG's ice shelf to quantify melting processes that need inclusion in ocean models, with a strong focus on the grounding zone, (2) analogous measurements on the glacier to validate processes governing grounding-line retreat, (3) coupling of these in situ measurements with novel, high-resolution space-borne observations, (4) building this new understanding into state-of-the-art ocean (MIT General Circulation Model and Imperial College Ocean Model) and ice sheet (WAVI) models to correctly simulate the TG system, (5) coupling the models and running with realistic present-day ocean forcing to project the state of TG basin over the next hundred years. The international team will use a range of techniques, from the well-established, such as using a hot-water drill to instrument the ice column and water column in the grounding zone, through to the cutting-edge, such as deploying a borehole deployable remotely operated vehicle to survey the grounding zone, and using phase-coherent radar to monitor ice strain and basal melt rates. The outcome of the project will be a more complete understanding of the TG system in the critical zone extending from a few kilometers inland of the grounding line, through the grounding zone, and out under the ice shelf.

BIGDATA: IA: COLLABORATIVE RESEARCH: INTELLIGENT SOLUTIONS FOR NAVIGATING BIG DATA FROM THE ARCTIC AND ANTARCTIC

PI: John Paden

Sponsor: National Science Foundation

09/01/2018-08/31/2022

The objective of this research is to investigate artificial intelligence (AI) solutions for data collected by the Center for Remote Sensing of Ice Sheets (CReSIS) in order to provide an intelligent data understanding to automatically mine and analyze the heterogeneous dataset collected by CReSIS. Significant resources have been and will be spent in collecting and storing large and heterogeneous datasets from expensive Arctic and Antarctic fieldwork (e.g. through NSF Big Idea: Navigating the New Arctic). While traditional analyses provide some insight, the complexity, scale, and multidisciplinary nature of the data necessitate advanced intelligent solutions. This project will allow domain scientists to automatically answer questions about the properties of the data, including ice thickness, ice surface, ice bottom, internal layers, ice thickness prediction, and bedrock visualization. The planned approach will advance the broader big data research community by improving the efficiency of deep learning methods and in the investigation of methods to merge data-driven AI approaches with application-specific domain knowledge. Special attention will be given to women and minority involvement in the research and the project will develop new course materials for several classes in AI at a Hispanic and minority serving institute.

In polar radar sounder imagery, the delineation of the ice top and ice bottom and layering within the ice is essential for monitoring and modeling the growth of ice sheets and sea ice. The optimal approach to this problem should merge the radar sounder data with physical ice models and related datasets such as ice coverage and concentration maps, spatiotemporal meteorological maps, and ice velocity. Rather than directly engineering specific relations into the image analysis that require many parameters to be defined and tuned, data-dependent approaches let the machine learn these relationships. To devise intelligent solutions for navigating the big data from the Arctic and Antarctic and to scale up the current and traditional techniques to big data, this project plans several approaches for detecting ice surface, bottom, internal layers, 3D modeling of bedrock and spatial-temporal monitoring of the ice surface: 1) Devise new methodologies based on hybrid networks combining machine learning with traditional domain specific knowledge and transforming the entire deep learning network to the time-frequency domain. 2) Equip the machine with information that is not visible to the human eye or that is hard for a human operator to consider simultaneously, to be able to detect internal layers and 3D basal topography on a large scale. Using the results of the feature tracking of the ice surface in radar altimetry, the research effort will also develop new data-dependent techniques for predicting the ice thickness for following years based on deep recurrent neural networks.

CENTER FOR REMOTE SENSING OF SNOW AND SOIL MOISTURE MEASUREMENT

Lead Institution: Alabama

KU PI: David Braaten

06/01/2018-05/31/2019