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Full-Text Articles in Engineering
Automatic Target Cueing Of Hyperspectral Image Data, Terry A. Wilson
Automatic Target Cueing Of Hyperspectral Image Data, Terry A. Wilson
Theses and Dissertations
Modern imaging sensors produce vast amounts data, overwhelming human analysts. One such sensor is the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) hyperspectral sensor. The AVIRIS sensor simultaneously collects data in 224 spectral bands that range from 0.4µm to 2.5µm in approximately 10nm increments, producing 224 images, each representing a single spectral band. Autonomous systems are required that can fuse "important" spectral bands and then classify regions of interest if all of this data is to be exploited. This dissertation presents a comprehensive solution that consists of a new physiologically motivated fusion algorithm and a novel Bayes optimal self-architecting classifier …
Representations, Approximations, And Algorithms For Mathematical Speech Processing, Laura R. Suzuki
Representations, Approximations, And Algorithms For Mathematical Speech Processing, Laura R. Suzuki
Theses and Dissertations
Representing speech signals such that specific characteristics of speech are included is essential in many Air Force and DoD signal processing applications. A mathematical construct called a frame is presented which captures the important time-varying characteristic of speech. Roughly speaking, frames generalize the idea of an orthogonal basis in a Hilbert space, Specific spaces applicable to speech are L2(R) and the Hardy spaces Hp(D) for p> 1 where D is the unit disk in the complex plane. Results are given for representations in the Hardy spaces involving Carleson's inequalities (and its extensions), …
Parallel Implementation Of An Artificial Neural Network Integrated Feature And Architecture Selection Algorithm, Craig W. Rizzo
Parallel Implementation Of An Artificial Neural Network Integrated Feature And Architecture Selection Algorithm, Craig W. Rizzo
Theses and Dissertations
The selection of salient features and an appropriate hidden layer architecture contributes significantly to the performance of a neural network. A number of metrics and methodologies exist for estimating these parameters. This research builds on recent efforts to integrate feature and architecture selection for the multilayer perceptron. In the first stage of work a current algorithm is developed in a parallel environment, significantly improving its efficiency and utility. In the second stage, improvements to the algorithm are proposed. With regards to feature selection, a common random number (CRN) addition is proposed. Two new methods of architecture selection are examined, to …
The Application Of Sequential Convex Programming To Large-Scale Structural Optimization Problems, Todd A. Sriver
The Application Of Sequential Convex Programming To Large-Scale Structural Optimization Problems, Todd A. Sriver
Theses and Dissertations
Structural design problems are often modeled using finite element methods. Such models are often characterized by constraint functions that are not explicitly defined in terms of the design variables. These functions are typically evaluated through numerical finite element analysis (FEA). Optimizing large-scale structural design models requires computationally expensive FEAs to obtain function and gradient values. An optimization approach which uses the SCP sequential convex programming algorithm of Zillober, integrated as the optimizer in the Automated Structural Optimization System (ASTROS), is tested. The traditional approach forms an explicitly defined approximate subproblem at each design iteration that is solved using the method …
Modified Multiple Model Adaptive Estimation (M3Ae) For Simultaneous Parameter And State Estimation, Mikel M. Miller
Modified Multiple Model Adaptive Estimation (M3Ae) For Simultaneous Parameter And State Estimation, Mikel M. Miller
Theses and Dissertations
In many estimation problems, it is desired to estimate system states and parameters simultaneously. However, inherent to traditional estimation architectures of the past, the designer has had to make a trade-off decision between designs intended for accurate state estimation versus designs concerned with accurate parameter estimation. This research develops one solution to this trade-off decision by proposing a new architecture based on Kalman filtering (KF) and Multiple Model Adaptive Estimation (MMAE) techniques. This new architecture, the Modified-MMAE (M3AE), exploits the benefits of an MMAE designed for accurate parameter estimation, and yet performs at least as well in state …
Improved Mathematical Modeling For Gps Based Navigation, Salvatore Nardi
Improved Mathematical Modeling For Gps Based Navigation, Salvatore Nardi
Theses and Dissertations
This thesis is concerned with the development of new closed form GPS position determination algorithms that work in the presence of pseudorange measurement noise. The mathematical derivation of two closed form algorithms, based on stochastic modeling and estimation techniques, is presented. The algorithms provide an estimate of the GPS solution parameters (viz., the user position and the user clock bias) as well as the estimation error covariance. The experimental results are analyzed by comparison to the baseline results from the conventional Iterative Least Squares (ILS) algorithm. In typical GPS scenarios, the closed form algorithms are extremely sensitive to noise, making …
Embedding A Reactive Tabu Search Heuristic In Unmanned Aerial Vehicle Simulations, Joel L. Ryan
Embedding A Reactive Tabu Search Heuristic In Unmanned Aerial Vehicle Simulations, Joel L. Ryan
Theses and Dissertations
We apply a Reactive Tabu Search (RTS) heuristic within a discrete event simulation to solve routing problems for Unmanned Aerial Vehicles (UAVs). Our formulation represents this problem as a multiple Traveling Salesman Problem with time windows (mTSPTW), with the objective of attaining a specified level of target coverage using a minimum number of vehicles. Incorporating weather and probability of UAV survival at each target as random inputs, the RTS heuristic in the simulation searches for the best solution in each realization of the problem scenario in order to identify those routes that are robust to variations in weather, threat, or …