QUEST Hierarchy for Hyperspectral Face Recognition

Face recognition is an attractive biometric due to the ease in which photographs of the human face can be acquired and processed. The non-intrusive ability of many surveillance systems permits face recognition applications to be used in a myriad of environments. Despite decades of impressive research in this area, face recognition still struggles with variations in illumination, pose and expression not to mention the larger challenge of willful circumvention. The integration of supporting contextual information in a fusion hierarchy known as QUalia Exploitation of Sensor Technology (QUEST) is a novel approach for hyperspectral face recognition that results in performance advantages and a robustness not seen in leading face recognition methodologies. This research demonstrates a method for the exploitation of hyperspectral imagery and the intelligent processing of contextual layers of spatial, spectral, and temporal information. This approach illustrates the benefit of integrating spatial and spectral domains of imagery for the automatic extraction and integration of novel soft features (biometric). The establishment of the QUEST methodology for face recognition results in an engineering advantage in both performance and efficiency compared to leading and classical face recognition techniques. An interactive environment for the testing and expansion of this recognition framework is also provided

The Science Performance of JWST as Characterized in Commissioning

This paper characterizes the actual science performance of the James Webb Space Telescope (JWST), as determined from the six month commissioning period. We summarize the performance of the spacecraft, telescope, science instruments, and ground system, with an emphasis on differences from pre-launch expectations. Commissioning has made clear that JWST is fully capable of achieving the discoveries for which it was built. Moreover, almost across the board, the science performance of JWST is better than expected; in most cases, JWST will go deeper faster than expected. The telescope and instrument suite have demonstrated the sensitivity, stability, image quality, and spectral range that are necessary to transform our understanding of the cosmos through observations spanning from near-earth asteroids to the most distant galaxies.Comment: 5th version as accepted to PASP; 31 pages, 18 figures; https://iopscience.iop.org/article/10.1088/1538-3873/acb29

Implementation of the Metaheuristic Tabu Search in Route Selection for Mobility Analysis Support System

This thesis employs a reactive tabu search heuristic implemented in the Java programming language to solve a real world variation of the vehicle routing problem with the objective of providing quality routes to Mobility Analysis Support System (MASS). MASS is a stochastic simulation model used extensively by Air Mobility Command (AMC) to analyze strategic airlift capabilities and future procurement decisions. This dynamic real world problem of strategic and tactical airlift possesses a number of side constraints such as vehicle capacities, route length and time windows in a sizeable network with multiple depots and a large fleet of heterogeneous vehicles. Finding optimal solutions to this problem is currently not practical. Currently, MASS requires all possible routes used in its simulation to be manually selected. As a result, the route selection process is a tedious and time consuming process that relies on experience and past performance of the model to obtain quality routes for the mobility system

QUEST Hierarchy for Hyperspectral Face Recognition

A qualia exploitation of sensor technology (QUEST) motivated architecture using algorithm fusion and adaptive feedback loops for face recognition for hyperspectral imagery (HSI) is presented. QUEST seeks to develop a general purpose computational intelligence system that captures the beneficial engineering aspects of qualia-based solutions. Qualia-based approaches are constructed from subjective representations and have the ability to detect, distinguish, and characterize entities in the environment Adaptive feedback loops are implemented that enhance performance by reducing candidate subjects in the gallery and by injecting additional probe images during the matching process. The architecture presented provides a framework for exploring more advanced integration strategies beyond those presented. Algorithmic results and performance improvements are presented as spatial, spectral, and temporal effects are utilized; additionally, a Matlab-based graphical user interface (GUI) is developed to aid processing, track performance, and to display results

Solving the Aerial Fleet Refueling Problem using Group Theoretic Tabu Search

The Aerial Fleet Refueling Problem (AFRP) is concerned with the efficient and effective use of a heterogeneous set of tanker (refueling) aircraft, located at diverse geographical locations, in the required operations associated with the deployment of a diverse fleet of military aircraft to a foreign theater of activity. Typically, the “receiving ” aircraft must traverse great distances over large bodies of water and/or over other inhospitable environs where no ground based refueling resources exist. Lacking the ability to complete their flights without refueling, each receiving aircraft must be serviced one or more times during their deployment flights by means of in-flight refueling provided by one of the available tanker aircraft. The receiving aircraft, aggregated into receiver groups (RGs) that fly together, have stipulated departure and destination bases and each RG’s arrival time is bounded by a stated desired earliest and latest time. The excellence of a suggested solution to this very challenging decision making problem is measured relative to a rigorously defined hierarchical multicriteria objective function. This paper describes how the AFRP for the Air Mobility Command (AMC), Scott AFB, IL is efficiently solved using Group Theoretic Tabu Search (GTTS). GTTS uses the symmetric group on n letters (Sn) and applies it to this problem using the Java TM language

The Potential Role of DNA Methylation in Abdominal Aortic Aneurysms

Abdominal aortic aneurysm (AAA) is a complex disorder that has a significant impact on the aging population. While both genetic and environmental risk factors have been implicated in AAA formation, the precise genetic markers involved and the factors influencing their expression remain an area of ongoing investigation. DNA methylation has been previously used to study gene silencing in other inflammatory disorders and since AAA has an extensive inflammatory component, we sought to examine the genome-wide DNA methylation profiles in mononuclear blood cells of AAA cases and matched non-AAA controls. To this end, we collected blood samples and isolated mononuclear cells for DNA and RNA extraction from four all male groups: AAA smokers (n = 11), AAA non-smokers (n = 9), control smokers (n = 10) and control non-smokers (n = 11). Methylation data were obtained using the Illumina 450k Human Methylation Bead Chip and analyzed using the R language and multiple Bioconductor packages. Principal component analysis and linear analysis of CpG island subsets identified four regions with significant differences in methylation with respect to AAA: kelch-like family member 35 (KLHL35), calponin 2 (CNN2), serpin peptidase inhibitor clade B (ovalbumin) member 9 (SERPINB9), and adenylate cyclase 10 pseudogene 1 (ADCY10P1). Follow-up studies included RT-PCR and immunostaining for CNN2 and SERPINB9. These findings are novel and suggest DNA methylation may play a role in AAA pathobiology