Award Data

TECHNICAL ABSTRACT: The U.S. High Frequency Radar (HFR) network contains more than 140 coastal stations that provide hourly two-dimensional coastal surface currents. Approximately one-third of these are Long Range (LR) systems that transmit in the 4–5.5 MHz frequency band with offshore ranges and resolutions of 160-220 km and 6 km, respectively. Currents provided by this network have numerous ap ...

TECHNICAL ABSTRACT: The Space weather-based Position error maps for TEC - On-line (SpoT-On) project will use GPS-GNSS based TEC data, integrated into the Global Assimilation of Ionospheric Measurements (GAIM) operational system at the Utah State University Space Weather Center to produce an order of magnitude improved TEC position correction maps. These will be publicly and globally accessible. A ...

TECHNICAL ABSTRACT: This paper proposes development of a low-cost water monitoring system wirelesslynetworked to upload data to FieldKit for cloud based data visualization and validation. This will enable citizen scientists to collect a variety of parameters including conductivity, temperature, depth, and ocean noise measurements on an adhoc basis while contributing to a large data repository whic ...

TECHNICAL ABSTRACT: A flow micro-photometer is proposed that can measure absorption and backscatter from single aquatic particles including phytoplankton, detritus and minerals. This instrument will make use of a novel adaptive diaphragm to define an analysis region of interest that exactly matches the size, shape and orientation of each particle being analyzed. A micro-fluidic chip will be used t ...

On this effort Toyon Research Corp. and The Pennsylvania State University are developing deep learning-based algorithms for object recognition and new class discovery in look-down infrared (IR) imagery. Our approach involves the development of a hybrid classifier that exploits both transfer learning and semi-supervised paradigms in order to maintain good generalization accuracy, especially when li ...

Signature Research, Inc. (SGR) and Michigan Technological University (MTU) propose a Phase I STTR effort to develop a learning algorithm which exploits the spatio-spectral characteristics inherent within IR imagery and motion imagery.Our archive of modelled and labeled data sets will allow our team to thoroughly capture the variable elements that will drive machine learning performance.The overall ...

To address the National Geospatial-Intelligence Agency (NGA) need for overhead imagery analysis algorithms that provide uncertaintymeasures for object recognition and aggregation, Intellisense Systems, Inc. (ISS) proposes to develop a new Variational Object Recognition andGrouping Network (VORGNet) system. It is based on the innovation of implementing a Bayesian convolutional neural network (CNN) ...

We propose to build ARGON, the ARet Ground-to-Overhead Network. The network will ingest analyst-supplied ground-level imagery ofobjects and retrieve instances of those objects in overhead collections, providing tips back to the analysts. A proprietary method of trainingthe network, leveraging in-house capabilities, data sources, and tools, will be critical to its success. During Phase I, we will p ...

Building interpretable, composable autonomous systems requires consideration of uncertainties in the decisions and detections theygenerate. Human analysts need accurate absolute measures of probability to determine how to interpret and use the sometimes noisy resultsof machine learning systems; and composable autonomous systems need to be able to propagate uncertainties so that later reasoningsyst ...

To address the NGA need for algorithms that fuse observables from over-flight operations and from ground sources to automatically estimatethe degradation of urban environments due to battle damage or natural disasters, Intellisense Systems, Inc. (ISS) proposes to develop a newBayesian Urban Degradation Assessment (BUDA) software system. It is based on the integration of multiple damage assessment ...