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Full-Text Articles in Engineering
Terahertz Imaging Of Thin Film Layers With Matched Field Processing, Scott Schecklman, Lisa M. Zurk
Terahertz Imaging Of Thin Film Layers With Matched Field Processing, Scott Schecklman, Lisa M. Zurk
Electrical and Computer Engineering Faculty Publications and Presentations
Terahertz (THz) time of flight (TOF) tomography systems offer a new measurement modality for non-destructive evaluation (NDE) of the subsurface layers of protective coatings and/or laminated composite materials for industrial, security and biomedical applications. However, for thin film samples, the time-of-flight within a layer is less than the duration of the THz pulse and consequently there is insufficient range resolution for NDE of the sample under test. In this paper, matched field processing (MFP) techniques are applied to thickness estimation in THz TOF tomography applications, and these methods are demonstrated by using measured THz spectra to estimate the the thicknesses …
Performance Metrics For Depth-Based Signal Separation Using Deep Vertical Line Arrays, Gabriel Paul Kniffin, John Kenneth Boyle, Lisa Zurk, Martin Siderius
Performance Metrics For Depth-Based Signal Separation Using Deep Vertical Line Arrays, Gabriel Paul Kniffin, John Kenneth Boyle, Lisa Zurk, Martin Siderius
Electrical and Computer Engineering Faculty Publications and Presentations
A recent publication by McCargar and Zurk [(2013). J. Acoust. Soc. Am. 133(4), EL320–EL325] introduced a modified Fourier transform-based method for passive source depth estimation using vertical line arrays deployed below the critical depth in the deep ocean. This method utilizes the depth-dependent modulation caused by the interference between the direct and surface-reflected acoustic arrivals, the observation of which is enhanced by propagation through the reliable acoustic path. However, neither the performance of this method nor its limits of applicability have yet been thoroughly investigated. This paper addresses both of these issues; the first by identifying and analyzing the factors …
Scale Invariant Object Recognition Using Cortical Computational Models And A Robotic Platform, Danny Voils
Scale Invariant Object Recognition Using Cortical Computational Models And A Robotic Platform, Danny Voils
Dissertations and Theses
This paper proposes an end-to-end, scale invariant, visual object recognition system, composed of computational components that mimic the cortex in the brain. The system uses a two stage process. The first stage is a filter that extracts scale invariant features from the visual field. The second stage uses inference based spacio-temporal analysis of these features to identify objects in the visual field. The proposed model combines Numenta's Hierarchical Temporal Memory (HTM), with HMAX developed by MIT's Brain and Cognitive Science Department. While these two biologically inspired paradigms are based on what is known about the visual cortex, HTM and HMAX …
An Intelligent Database For Psubot, An Autonomous Wheelchair, Dieudonne Mayi
An Intelligent Database For Psubot, An Autonomous Wheelchair, Dieudonne Mayi
Dissertations and Theses
In the design of autonomous mobile robots, databases have been used mainly to store information on the environment in which the device is to operate. For most of the models and ready systems, the database when used, is not a stand alone component in the system, rather it is only intended to keep static information on the disposition and properties of objects on the map.
An Approach To Pattern Recognition Of Multifont Printed Alphabet Using Conceptual Graph Theory And Neural Networks, Ihab A. Harb
An Approach To Pattern Recognition Of Multifont Printed Alphabet Using Conceptual Graph Theory And Neural Networks, Ihab A. Harb
Dissertations and Theses
This thesis describes an approach for accomplishing a pattern recognition task using conceptual graph theory and neural networks (NNs). The set of patterns to be recognized are the capital letters of six different fonts of the English alphabet, plus two shifted and six rotated versions of each. The letters are represented to the neural network on a 16x16 input grid (256 "sensor lines"). A standard classification encoding for such patterns is to use a 26-bit vector (26 lines at the NN's output), one bit corresponding to each letter. Experiments with such an encoding yielded results with poor generalization capability. A …
Invariant Pattern Recognition Algorithm Using The Hough Transform, Duwang Li
Invariant Pattern Recognition Algorithm Using The Hough Transform, Duwang Li
Dissertations and Theses
A new algorithm is proposed which uses the Hough Transform to recognize two dimensional objects independent of their orientations, sizes and locations. The binary image of an object is represented by a set of straight lines. Features of the straight lines, namely the lengths and the angles of their normals, their lengths and the end point positions are extracted using the Hough Transform. A data structure for the extracted lines is constructed so that it is efficient to match the features of the lines of one object to those of another object, and determine if one object is a rotated …
Two New Parallel Processors For Real Time Classification Of 3-D Moving Objects And Quad Tree Generation, Farjam Majd
Two New Parallel Processors For Real Time Classification Of 3-D Moving Objects And Quad Tree Generation, Farjam Majd
Dissertations and Theses
Two related image processing problems are addressed in this thesis. First, the problem of identification of 3-D objects in real time is explored. An algorithm to solve this problem and a hardware system for parallel implementation of this algorithm are proposed. The classification scheme is based on the "Invariant Numerical Shape Modeling" (INSM) algorithm originally developed for 2-D pattern recognition such as alphanumeric characters. This algorithm is then extended to 3-D and is used for general 3-D object identification. The hardware system is an SIMD parallel processor, designed in bit slice fashion for expandability. It consists of a library of …