Knowledge-Based Aircraft Automation: Managers Guide on the use of Artificial Intelligence for Aircraft Automation and Verification and Validation Approach for a Neural-Based Flight Controller

The ultimate goal of this report was to integrate the powerful tools of artificial intelligence into the traditional process of software development. To maintain the US aerospace competitive advantage, traditional aerospace and software engineers need to more easily incorporate the technology of artificial intelligence into the advanced aerospace systems being designed today. The future goal was to transition artificial intelligence from an emerging technology to a standard technology that is considered early in the life cycle process to develop state-of-the-art aircraft automation systems. This report addressed the future goal in two ways. First, it provided a matrix that identified typical aircraft automation applications conducive to various artificial intelligence methods. The purpose of this matrix was to provide top-level guidance to managers contemplating the possible use of artificial intelligence in the development of aircraft automation. Second, the report provided a methodology to formally evaluate neural networks as part of the traditional process of software development. The matrix was developed by organizing the discipline of artificial intelligence into the following six methods: logical, object representation-based, distributed, uncertainty management, temporal and neurocomputing. Next, a study of existing aircraft automation applications that have been conducive to artificial intelligence implementation resulted in the following five categories: pilot-vehicle interface, system status and diagnosis, situation assessment, automatic flight planning, and aircraft flight control. The resulting matrix provided management guidance to understand artificial intelligence as it applied to aircraft automation. The approach taken to develop a methodology to formally evaluate neural networks as part of the software engineering life cycle was to start with the existing software quality assurance standards and to change these standards to include neural network development. The changes were to include evaluation tools that can be applied to neural networks at each phase of the software engineering life cycle. The result was a formal evaluation approach to increase the product quality of systems that use neural networks for their implementation

Experimental Confirmation of the General Solution to the Multiple Phase Matching Problem

We recently described a general solution to the phase matching problem that arises when one wishes to perform an arbitrary number of nonlinear optical processes in a single medium [PRL 95 (2005) 133901]. Here we outline in detail the implementation of the solution for a one dimensional photonic quasicrystal which acts as a simultaneous frequency doubler for three independent optical beams. We confirm this solution experimentally using an electric field poled KTiOPO$_4$ crystal. In optimizing the device, we find - contrary to common practice - that simple duty cycles of 100% and 0% may yield the highest efficiencies, and show that our device is more efficient than a comparable device based on periodic quasi-phase-matching

HIV Infections and Dendritic Cells in a Post-Antiretroviral Therapy Era

Objective. To determine whether gene expression in myeloid dendritic cells (mDC) is correlated to the size of the human immunodeficiency virus (HIV) reservoir in CD4 T cells during antiretroviral therapy (ART)-treated HIV infections. Background. mDC are innate immune cells that respond to viral infections. One barrier to curing HIV infections is the existence of latent reservoirs after HIV integrates into the genome of CD4 T cells. There is a critical need to examine the role of mDC in viral reservoir maintenance to inform strategies to counteract HIV latency. Methods. We will quantify the HIV reservoir in CD4 T cells and assess the gene expression of mDCs from successfully ART-treated (ST) individuals. Using these data, we will develop a computational approach to model the relationship between mDC gene expression and the size of HIV reservoirs in CD4 T cells. Results. We identified genes and pathways in mDC that correlated with HIV reservoir levels in CD4 T cells, which differed depending on treatment status. We anticipate that a larger cohort of ST subjects will enable the further identification of genes and pathways of mDC that correlate with HIV reservoir levels. Conclusion. These results will inform targets for the control or elimination of latent reservoirs in HIV curative approaches. This work has the potential to identify new biomarkers for determining reservoir size and inform strategies such as DC vaccination regimes for eradicating viral reservoirs during HIV infections. Grants. The Institute of AIDS and Emerging Infectious Diseases (IAEID) Pilot Grant, Florida Department of Health, awarded to Shannon Murray