Award Data

Nanohmics proposes to develop a chip-scale inertial measurement unit (IMU) for munitions applications. The miniaturization is key to extreme-G survivability and operation. Models and simulations will provide primary support for the feasibility and a hardware demonstration will provide additional proof-of-concept and improve the program risk assessment prior to a Phase II program.

In implementing an electro-optic (EO) seeker, the selection of a waveband or wavebands is the major decision for the system design. The visible and near-infrared wavebands work well in the daytime when the atmosphere is clear, but passive imaging systems in these bands are not useful at night, and they are susceptible to scattering in fog, smoke, and dusty environments. The mid-wave infrared (MWIR ...

The development of a guided hypervelocity projectile (HVP) must surmount a number of significant technical challenges. Texas Research Institute Austin (TRI Austin) proposes to address leading edge/control surface challenges, sabot design, and the sub-projectile issues through the selection of materials, material processes, and component manufacturing methods. TRI Austin has assembled a team of tec ...

Black River Systems is developing real-time fire control tracking and threat prediction capabilities for land and sea - based missile defense. Our nation faces a rapidly evolving set of missile threats that exhibit extreme velocities, high dynamic motion and difficult to predict maneuverability. There are many different trajectories and many types of threats: subsonic, supersonic; sea-skimming, la ...

Deep-learning Ephemeral-Pad Encryption Neural Networks (DEPENN) software applies one-time pad and quantized neural network technologies to provide efficient, moving-target encryption of all data associated with neural network operations. DEPENN overcomes the significant computational burden and technical challenges of fully homomorphically encrypted neural network operation while providing compara ...

Homomorphic Encryption (HE) allows for training and deployment of neural networks on encrypted data. Furthermore, HE allows for encryption of neural network model parameters such as weights. Thus, HE provides robustness against both black-box and white-box attacks. In the emerging cloud-based AI environments with edge computing nodes, HE enables privacy-preserving training and deployment of neural ...

We propose a quantum adversarial machine learning (QAML) approach that combines ideas from different classical AML techniques such as Defense-GAN and thermometer-encoding of inputs. We propose an implementation of Defense-GAN on the D-Wave Leap quantum computing environment. We also propose to leverage ideas from quantum information science such as noisy inputs/outputs/parameters to improve the ro ...

This project will develop a new scientific instrument optimized for the advanced characterization of near-infrared fluorescent nanoparticles that can exist as left- or right-handed structures (enantiomers). Single-walled carbon nanotubes (SWCNTs) are currently the leading example of such nanomaterials. Applied NanoFluorescence, LLC (ANF) proposes a novel multi-mode chiroptical spectrometer that ca ...

Additive manufacturing (AM) is a promising manufacturing technique for end-use parts that can solve challenges for American manufacturers in many industries, e.g. aerospace, defense, automotive, energy, and healthcare. However, despite the potential that AM offers, the rate of AM adoption in industry is very slow. This is because AM suffers from low repeatability and quality consistency issues, wh ...

This Phase II SBIR project will continue the development of an optical sorter that will be used to estimate drop sizes in sprays. The optical sorter will determine the size of drops in the 0.1 to 10 microns range. Drops in this size range are prevalent in the automobile industry, where the fuel injection pressures have increased tremendously over the past two decades. These newer injectors provide ...