Award Data

krtkl (“critical”) will conduct a Phase I Feasibility Study to identify the best approach for reducing aviator cognitive load by optimizing information delivery and decision-making based on a thorough analysis of existing platforms, sensors, data sources, and onboard compute resources. This information will be used to identify Artificial Intelligence and Machine Learning based algorithms for p ...

Metal Additive Manufacturing (AM) has the potential to reduce DoD sustainment costs and delays through on demand part production. However, at present metal AM build times are too long, often require post-processing treatments, and produce parts of inconsistent quality or with defects. There are multiple factors inherent to LPBF that contribute to the variability of the build and cause defects. Thi ...

Ultra-Wide bandgap materials such as GaN and Ga2O3 are emerging as preferred materials in high power applications due to their high breakdown field. The thermal dissipation is poor in both those materials due to low thermal conductivity. A high thermal conductivity material such as SiC is used as a growing substrate, however, the thermal conductivity is still limited due to defects in the interfac ...

Sensor systems aboard aircrafts address unique problems and are siloed in their objectives. A data silo is a term used to describe a data system that is insulated from other data systems. While keeping information categorized may lead to easier organization, the costs often outweigh the benefits. In aviation systems, data silos often lead to miscommunication, cognitive overload, and waste. These d ...

In manned aviation environments – both military and commercial – AI support is being developed and researched for ground-based planning and operational decision support. Using AI in real-time with crews poses additional questions and issues. This research will provide the means to understand the measures and requirements to architect potential AI-Agent solutions before implementation. This fea ...

Deep Convolutional Neural Networks (DCNNs) have become ubiquitous in the analysis of large datasets with geometric symmetries. These datasets are common in medicine, science, intelligence, autonomous driving and industry. While analysis based on DCNNs have proven powerful, uncertainty estimation for such analyses has required sophisticated empirical studies. This has negatively impacted the effect ...

Modular continuous processes are the future of chemical process development and chemical manufacturing. They enable faster time to market, improved safety, lower costs, and enhanced flexibility over traditional batch processes. Proposed work under this topic includes the design of a modular system for synthesis of many energetic materials, energetic precursors, and critical chemicals. Modules that ...

Multi-Dimensional Event Sourcing & Correlation - Publicly Available Information (PAI) (MDESC-P) will support collection jointly across disparate PAI sources with coordinated cueing of more constrained intelligence, surveillance, target acquisition, and reconnaissance (ISTAR) sources. The primary objective for MDESC-P is to deliver a scalable and automated PAI collection management solution using a ...

Deep Labs recognizes USSOCOM’s challenge to process multiple data and communications inputs for optimized decision making, and to support rapid on-the-move abilities to learn and communicate knowledge to enhance tactically relevant situational awareness in peer/near peer environments. Deep Labs has proven this capability across complex challenges in the world’s largest commercial enterprises a ...

As electronic warfare (EW) permeates down to the small unit operations (e.g., with the proliferation of IoT devices, 5G, demand for multidomain spectrum management, and adversaries who can leverage or attack these components with malice), there is a need to bolster Operator's shared situation awareness (SSA) of the EW space that overlays the physical battlefield to enable a small unit multidomain ...