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Articles 1 - 22 of 22
Full-Text Articles in Engineering
Towards The Mitigation Of Correlation Effects In The Analysis Of Hyperspectral Imagery With Extension To Robust Parameter Design, Jason P. Williams
Towards The Mitigation Of Correlation Effects In The Analysis Of Hyperspectral Imagery With Extension To Robust Parameter Design, Jason P. Williams
Theses and Dissertations
Standard anomaly detectors and classifiers assume data to be uncorrelated and homogeneous, which is not inherent in Hyperspectral Imagery (HSI). To address the detection difficulty, a new method termed Iterative Linear RX (ILRX) uses a line of pixels which shows an advantage over RX, in that it mitigates some of the effects of correlation due to spatial proximity; while the iterative adaptation from Iterative Linear RX (IRX) simultaneously eliminates outliers. In this research, the application of classification algorithms using anomaly detectors to remove potential anomalies from mean vector and covariance matrix estimates and addressing non-homogeneity through cluster analysis, both of …
Computer Aided Multi-Data Fusion Dismount Modeling, Juan L. Morales
Computer Aided Multi-Data Fusion Dismount Modeling, Juan L. Morales
Theses and Dissertations
Recent research efforts strive to address the growing need for dismount surveillance, dismount tracking and characterization. Current work in this area utilizes hyperspectral and multispectral imaging systems to exploit spectral properties in order to detect areas of exposed skin and clothing characteristics. Because of the large bandwidth and high resolution, hyperspectral imaging systems pose great ability to characterize and detect dismounts. A multi-data dismount modeling system where the development and manipulation of dismount models is a necessity. This thesis demonstrates a computer aided multi-data fused dismount model, which facilitates studies of dismount detection, characterization and identification. The system is created …
Overcoming Pose Limitations Of A Skin-Cued Histograms Of Oriented Gradients Dismount Detector Through Contextual Use Of Skin Islands And Multiple Support Vector Machines, Jonathon R. Climer
Overcoming Pose Limitations Of A Skin-Cued Histograms Of Oriented Gradients Dismount Detector Through Contextual Use Of Skin Islands And Multiple Support Vector Machines, Jonathon R. Climer
Theses and Dissertations
This thesis provides a novel visualization method to analyze the impact that articulations in dismount pose and camera aspect angle have on histograms of oriented gradients (HOG) features and eventual detections. Insights from these relationships are used to identify limitations in a state of the art skin cued HOG dismount detector's ability to detect poses not in a standard upright stances. Improvements to detector performance are made by further leveraging available skin information, reducing false detections by an additional order of magnitude. In addition, a method is outlined for training supplemental support vector machines (SVMs) from computer generated data, for …
Applying Image Matching To Video Analysis, Adam J. Behring
Applying Image Matching To Video Analysis, Adam J. Behring
Theses and Dissertations
Dealing with the volume of multimedia collected on a daily basis for intelligence gathering and digital forensics investigations requires significant manual analysis. A component of this problem is that a video may be reanalyzed that has already been analyzed. Identifying duplicate video sequences is difficult due to differences in videos of varying quality and size. This research uses a kd-tree structure to increase image matching speed. Keypoints are generated and added to a kd-tree of a large dimensionality (128 dimensions). All of the keypoints for the set of images are used to construct a global kd-tree, which allows nearest neighbor …
Flexible Computing Architecture For Real Time Skin Detection, Matthew P. Hornung
Flexible Computing Architecture For Real Time Skin Detection, Matthew P. Hornung
Theses and Dissertations
In both the Air Force and Search and Rescue Communities, there is a current need to detect and characterize persons. Existing methods use red-green-blue (RGB) imagery, but produce high false alarm rates. New technology in multi-spectral skin detection is better than the existing RGB methods, but lacks a control and processing architecture to make them efficient for real time problems. We hypothesize that taking a minimalistic approach to the software design, we can perform image preprocessing, feature computation, and skin detection in real time. A number of applications require accurate detection and characterization of persons, human measurement and signature intelligence …
Real Time Fault Detection And Diagnostics Using Fpga-Based Architecture, Nathan P. Naber
Real Time Fault Detection And Diagnostics Using Fpga-Based Architecture, Nathan P. Naber
Theses and Dissertations
Errors within circuits caused by radiation continue to be an important concern to developers. A new methodology of real time fault detection and diagnostics utilizing FPGA based architectures while under radiation were investigated in this research. The contributions of this research are focused on three areas; a full test platform to evaluate a circuit while under irradiation, an algorithm to detect and diagnose fault locations within a circuit, and finally to characterize Triple Design Triple Modular Redundancy (TDTMR), a new form of TMR. Five different test setups, injected fault test, gamma radiation test, thermal radiation test, optical laser test, and …
Improved Multispectral Skin Detection And Its Application To Search Space Reduction For Dismount Detection Based On Histograms Of Oriented Gradients, Adam L. Brooks
Theses and Dissertations
Due to the general shift from conventional warfare to terrorism and urban warfare by enemies of the United States in the late 20th Century, locating and tracking individuals of interest have become critically important. Dismount detection and tracking are vital to provide security and intelligence in both combat and homeland defense scenarios including base defense, combat search and rescue (CSAR), and border patrol. This thesis focuses on exploiting recent advances in skin detection research to reliably detect dismounts in a scene. To this end, a signal-plus-noise model is developed to map modeled skin spectra to the imaging response of an …
A Hybrid Templated-Based Composite Classification System, Michael A. Turnbaugh
A Hybrid Templated-Based Composite Classification System, Michael A. Turnbaugh
Theses and Dissertations
An automatic target classification system contains a classifier which reads a feature as an input and outputs a class label. Typically, the feature is a vector of real numbers. Other features can be non-numeric, such as a string of symbols or alphabets. One method of improving the performance of an automatic classification system is through combining two or more independent classifiers that are complementary in nature. Complementary classifiers are observed by finding an optimal method for partitioning the problem space. For example, the individual classifiers may operate to identify specific objects. Another method may be to use classifiers that operate …
Use Of Tabu Search In A Solver To Map Complex Networks Onto Emulab Testbeds, Jason E. Macdonald
Use Of Tabu Search In A Solver To Map Complex Networks Onto Emulab Testbeds, Jason E. Macdonald
Theses and Dissertations
The University of Utah's solver for the testbed mapping problem uses a simulated annealing metaheuristic algorithm to map a researcher's experimental network topology onto available testbed resources. This research uses tabu search to find near-optimal physical topology solutions to user experiments consisting of scale-free complex networks. While simulated annealing arrives at solutions almost exclusively by chance, tabu search incorporates the use of memory and other techniques to guide the search towards good solutions. Both search algorithms are compared to determine whether tabu search can produce equal or higher quality solutions than simulated annealing in a shorter amount of time. It …
Multi-Dimensional Classification Algorithm For Automatic Modulation Recognition, Ouail Albairat
Multi-Dimensional Classification Algorithm For Automatic Modulation Recognition, Ouail Albairat
Theses and Dissertations
This thesis proposes an approach for modulation classification using existing features in a more efficient way. The Multi-Dimensional Classification Algorithm (MDCA) treats features extracted from signals of interest as elements with irrelevant identities, hence eliminating any dependence of the classifier on any particular feature. This design enables the use of any number of features, and the MDCA algorithm provides the capability to classify modulations in higher dimensions. The use of multiple features requires an equal number of data dimensions, and thus classification in as high a dimensional space as possible can improve final classification results. Finally, the MDCA algorithm uses …
Automatic Target Recognition User Interface Tool, David A. Kerns
Automatic Target Recognition User Interface Tool, David A. Kerns
Theses and Dissertations
A computer tool to aid in selecting the best Automatic Target Recognition (ATR) algorithm is developed. The program considers many quantifiers, accepts user-defined parameters, allows for changes in the operational environment and presents results in a meaningful way. It is written for Microsoft Excel. An ATR algorithm assigns a class label to a recognized target. General designations can include "Friend" and "Foe." The error of designating "Friend" as "Foe" as well as "Foe" as "Friend" comes with a high cost. Studying each algorithm's error can minimize this cost. Receiver Operating Characteristic (ROC) curves provide only information on the probabilities given …
Daytime Detection Of Space Objects, Alistair D. Funge
Daytime Detection Of Space Objects, Alistair D. Funge
Theses and Dissertations
Space Situational Awareness (SSA) requires repeated object updates for orbit accuracy. Detection of unknown objects is critical. A daytime model was developed that evaluated sun flares and assessed thermal emissions from space objects. Iridium satellites generate predictable sun glints. These were used as a model baseline for daytime detections. Flares and space object thermal emissions were examined for daytime detection. A variety of geometric, material and atmospheric characteristics affected this daytime detection capability. In a photon noise limited mode, simulated Iridium flares were detected. The peak Signal-to- Noise Ratios (SNR) were 6.05e18, 9.63e5, and 1.65e7 for the nighttime, daytime and …
Modification Of A Modulation Recognition Algorithm To Enable Multi-Carrier Recognition, Angela M. Waters
Modification Of A Modulation Recognition Algorithm To Enable Multi-Carrier Recognition, Angela M. Waters
Theses and Dissertations
Modulation recognition is important for both military and commercial communication applications, particularly in cases where enhanced situation awareness and/or channel assessment is required to mitigate intentional or collateral interference. Modulation recognition via template matching or statistical analysis is a key aspect of non-cooperative (nonmatched filtering) signal interception, classification, and exploitation. This research concerns the evaluation and modification of a conventional Digitally Modulated Signal Recognition Algorithm (DMRA) to enable multi-carrier, Orthogonal Frequency Division Multiplexing (OFDM), waveform recognition. The original DMRA architecture was developed to classify communication signals for three fundamental data modulations, i.e., Amplitude Shift Keying (ASK), Phase Shift Keying (PSK), …
Target Recognition Using Late-Time Returns From Ultra-Wideband, Short-Pulse Radar, Kenneth J. Pascoe
Target Recognition Using Late-Time Returns From Ultra-Wideband, Short-Pulse Radar, Kenneth J. Pascoe
Theses and Dissertations
The goal of this research is to develop algorithms that recognize targets by exploiting properties in the late-time resonance induced by ultra-wide band radar signals. A new variant of the Matrix Pencil Method algorithm is developed that identifies complex resonant frequencies present in the scattered signal. Kalman filters are developed to represent the dynamics of the signals scattered from several target types. The Multiple Model Adaptive Estimation algorithm uses the Kalman filters to recognize targets. The target recognition algorithm is shown to be successful in the presence of noise. The performance of the new algorithms is compared to that of …
Target Recognition Using Linear Classification Of High Range Resolution Radar Profiles, Ricardo A. Diaz
Target Recognition Using Linear Classification Of High Range Resolution Radar Profiles, Ricardo A. Diaz
Theses and Dissertations
High Range Resolution (HRR) radar profiles map three-dimensional target characteristics onto one-dimensional signals that represent reflected radar intensity along target extent. In this thesis, second through fourth statistical moments are extracted from HRR profiles and input to Fisher Linear Discriminant (FLD) classifiers. An iterative classification process is applied that gradually minimizes required a priori knowledge about the target data. It is found that the second through fourth statistical moments of HRR profiles are useful features in the FLD classification of dissimilar targets and they provide reasonable discrimination of similar targets. Greater than 69% correct classification for two-target scenarios and greater …
Classification Of Radar Targets Using Invariant Features, Gregory J. Meyer
Classification Of Radar Targets Using Invariant Features, Gregory J. Meyer
Theses and Dissertations
Automatic target recognition ATR using radar commonly relies on modeling a target as a collection of point scattering centers, Features extracted from these scattering centers for input to a target classifier may be constructed that are invariant to translation and rotation, i.e., they are independent of the position and aspect angle of the target in the radar scene. Here an iterative approach for building effective scattering center models is developed, and the shape space of these models is investigated. Experimental results are obtained for three-dimensional scattering centers compressed to nineteen-dimensional feature sets, each consisting of the singular values of the …
Characterizing Data Streams Over Ieee 802.11b Ad-Hoc Wireless Networks, John T. Wagnon
Characterizing Data Streams Over Ieee 802.11b Ad-Hoc Wireless Networks, John T. Wagnon
Theses and Dissertations
Soon, advancements in data encryption technology will make real-time decryption of the contents of network packets virtually impossible. This research anticipates this development and extracts useful information based on packet level characteristics. Distinguishing characteristics from e-mail, HTTP, print, and FTP applications are identified and analyzed. The analysis of collected data from an ad-hoc wireless network reveals that distinguishing characteristics of network traffic do indeed exist. These characteristics include packet size, packet frequency, inter-packet correlation, and channel utilization. Without knowing the contents of packets or the direction of the traffic flow, the applications accessing the wireless network can be determined.
A Distributed Agent Architecture For A Computer Virus Immune System, Paul K. Harmer
A Distributed Agent Architecture For A Computer Virus Immune System, Paul K. Harmer
Theses and Dissertations
Information superiority is identified as an Air Force core competency and is recognized as a key enabler for the success of future missions. Information protection and information assurance are vital components required for achieving superiority in the Infosphere, but these goals are threatened by the exponential birth rate of new computer viruses. The increased global interconnectivity that is empowering advanced information systems is also increasing the spread of malicious code and current anti-virus solutions are quickly becoming overwhelmed by the burden of capturing and classifying new viral stains. To overcome this problem, a distributed computer virus immune system (CVIS) based …
Space Object Identification Using Feature Space Trajectory Neural Networks, Neal W. Bruegger
Space Object Identification Using Feature Space Trajectory Neural Networks, Neal W. Bruegger
Theses and Dissertations
The Feature Space Trajectory Neural Network (FSTNN) is a simple yet powerful pattern recognition tool developed by Neiberg and Casasent for use in an Automatic Target Recognition System. Since the FSTNN was developed, it has been used on various problems including speaker identification and space object identification. However, in these types of problems, the test set represents time series data rather than an independent set of points. Since the distance metric of the standard FSTNN treats each test point independently without regard to its position in the sequence, the FSTNN can yield less than optimal results in these problems. Two …
Decision Boundary Analysis Feature Selection For Breast Cancer Diagnosis, Daniel W. Gregg
Decision Boundary Analysis Feature Selection For Breast Cancer Diagnosis, Daniel W. Gregg
Theses and Dissertations
The general pattern recognition problem always involves the extraction of features to be used in pattern classification. There are no theoretical limitations to the number of features which can be obtained for a given pattern recognition problem. This research will develop a correlation procedure for screening a large feature set without the use of a trained classifier. The results will be compared to established saliency metrics such as the Fisher ratio and derivative-based techniques such as Ruck's saliency.
Space Object Identification Using Spatio-Temporal Pattern Recognition, Gary W. Brandstrom
Space Object Identification Using Spatio-Temporal Pattern Recognition, Gary W. Brandstrom
Theses and Dissertations
This thesis is part of a research effort to automate the task of characterizing space objects or satellites based on a sequence of images. The goal is to detect space object anomalies. Two algorithms are considered - the feature space trajectory neural network (FST NN) and hidden Markov model (HMM) classifier. The FST NN was first presented by Leonard Neiberg and David P. Casasent in 1994 as a target identification tool. Kenneth H. Fielding and Dennis W. Ruck recently applied the hidden Markov model classifier to a 3D moving light display identification problem and a target recognition problem, using time …
Spatio-Temporal Pattern Recognition Using Hidden Markov Models, Kenneth H. Fielding
Spatio-Temporal Pattern Recognition Using Hidden Markov Models, Kenneth H. Fielding
Theses and Dissertations
A new spatio-temporal method for identifying 3D objects found in 2D image sequences is presented. The Hidden Markov Model technique is used as a spatio-temporal classification algorithm to identify 3D objects by the temporal changes in observed shape features. A new information theoretic argument is developed that proves identifying objects based on image sequences can lead to higher classification accuracies than single look methods. A new distance measure is proposed that analyzes the performance of Hidden Markov Models in a multi-class pattern recognition problem. A three class problem identifying moving light display objects provides experimental verification of the sequence processing …