Award Data

Prixarc proposes to develop and commercialize novel terahertz (THz) focal-plane array (FPA) that utilizes 3D microstructures, smart readout integrated circuits, and allows efficient integration with processors that incorporate machine learning to increase the data collection efficiency. We plan to collaborate with University of Miami (UM) and Kansas State University (KSU) for this project. We prop ...

Reducing the False Alarm Rate (FAR) of Automated Target Recognition (ATR) algorithms for Synthetic Aperture Radar (SAR) imagery is crucial for Intelligence, Surveillance, Reconnaissance (ISR) and precision target engagement missions. While modern Deep Learning (DL) ATR networks have demonstrated advanced predictive capabilities and generalization for SAR imagery, they lack spatial awareness, resul ...

Reducing the false alarm rate (FAR) of Automated Target Recognition (ATR) algorithms is crucial for intelligence, surveillance, reconnaissance (ISR) and precision target engagement missions. There are many contributing factors that result in higher FAR for deep learning (DL) ATR networks operating on Synthetic Aperture Radar (SAR) imagery, including: image distortions, unrepresentative target sign ...

The amount of data collected from the suite of current and future sensors far surpasses the bandwidth of analysts to processes the data streams into actionable intelligence. This pixel to pupil ratio problem is a forcing function for developing robust algorithms which accurately find non-cooperative objects while minimizing the false alarm rate.  As exploitation algorithms are tasked with perform ...

The need for automated labelling of overhead data is obvious, less obvious is that these unlabelled images provide an opportunity to improve autonomous labelers making them more accurate and more dynamic. Extracting even a small amount of information from the stream of unlabelled samples has the potential to massively impact the quality of machine learners for remotely sensed imagery. The proposer ...

Modern Artificial Intelligence (AI) solutions generally employ carefully-crafted Neural Networks (NNs) that require extensive human effort to perform detection, identification, and annotation on each image to create training datasets. AI tools are desired that are optimized for object identification and annotation across diverse families of image data, are reliable and robust, not dependent on e ...

Correctly labeled data is essential for training AI/ML-based automatic target recognition (ATR). The training process is all the more complicated in synthetic aperture radar (SAR) images because of their unique phenomenology, such as orientation-sensitive target signatures, layover, cross-range smearing, and radio frequency interference. New automated technology must reduce the cost and acc ...

Expansion of aquaculture production depends crucially on the development of technologies that are able to add uncrewed management capabilities to fish farming operations, i.e., the ability to control the tank’s population remotely and without human presence. The present project aims to expand our successful Phase I feasibility research toward commercializing an autonomous device that can be inse ...

Expansion of aquaculture production depends crucially on the development of technologies that are able to perform functions important in a fish farming facility, without human input. For instance, early detection of a disease or parasite outbreak is critical in intensive aquaculture settings. Parasites, such as Sea Lice in Salmonid aquaculture are responsible for large losses. Existing options for ...

The Ocean Color and Cloud Monitor (OCCaM) is a hyperspectral based instrument suite with ocean color and cloud observational capabilities. Following the successful Phase I SBIR design period, the Phase II effort will focus on the integration of the Phase I design into a benchtop unit for testing and validation of the design concept. The payload was designed to meet the SWaP requirements of a comme ...