Deep Learning has proven to be an effective method for making highly accurate predictions from complex data sources. Convolutional neural networks continue to dominate image classification problems and recursive neural networks have proven their utility in caption generation and language translations. While these approaches are powerful, they do not offer explanation for how the output is generated. Without understanding how deep learning arrives at a solution there is no guarantee that these networks will transition from controlled laboratory environments to fieldable systems. This paper presents an approach for incorporating such rule based methodology into neural networks by embedding fuzzy inference systems into deep learning networks.
Over the last few decades, we have seen an increase in both quality and quantity of 3D data sets. These data sets primarily come in the form of discrete points that are projected onto the surface of the object (point clouds) and are often derived from either LIDAR data (in which case, the surface points are actively sensed) or stereoscopic pairs (in which case, the surface points are derived using two dimensional (2D) feature matching algorithms). As these data sets become larger and denser, they also become harder to sift through which demands methods for automatic object classification through computer vision processes. In this paper we revisit a method of recognizing objects from their surface features known as Tripod Operators.[1] More specifically, we explore how matching multiple features from an unknown object to a known shape allows us to determine the extent to which the objects are similar using the resultant Digital Elevation Model (DEM) or Surface Elevation Model (SEM) that results from manipulation of point clouds.. We apply this method to determine how to separate objects of various classes.
We address the problem of searching large amounts of 3D point set data for specific objects of interest, as characterized
by their surface shape. Motivating applications include the detection of ambush weapons from a convoy and the search
for objects of interest on the ground from an aircraft. Such data can occur in the form of relatively unstructured point
sets or range images, and can be derived from a variety of sensors. We study here the performance of Tripod Operators
(TOs) on synthetic range image data containing the shape of an oil drum; a cylinder with planar top. Tripod Operators
are an efficient method of extracting coordinate invariant shape information from surface shape representations using
discrete samples extracted in a specially constrained manner. They can be used in a variety of ways as components of a
system which performs detection, recognition and localization of objects based on their surface shape. We present
experimental results which characterize the approximate accuracy of detection of the test shape as a function of the
accuracy of the surface shape data. This is motivated by the need for an estimate of the required accuracy of 3D
surveillance data to enable detection of specific shapes.
Characterizing atmospheric turbulence through modeling dates back to the 1960's. For decades scientists have
studied how to mitigate the effects of the atmospheric turbulence on communications and imaging systems, but learning
how to use those properties of the atmosphere instead of mitigate them raise new challenges. Due to the fact that
atmospheric turbulence is inherently a random process, it can be an ideal "key generator" for strongly secure information
transfer. The purpose of this effort is to investigate to what extent the atmospheric turbulence can be exploited as a
robust random number generator. In this paper we report the progress in characterizing the atmosphere and a random bitstream
generator.
Modeling and simulating the atmosphere in a controlled environment has been a study of interest to
scientists for decades. The development of new technologies allows scientists to perform this task in a more realistic
and controlled environment and provides a powerful tool for the study and better understanding of the propagation
of light through the atmosphere. Technologies like Free-space laser communications (FSLC) and/or studies on light
propagation through the atmosphere are areas which constantly benefit from breakthroughs in the development of
atmospheric turbulence simulators. In this paper we present the results of the implementation of a phase only spatial
light modulator (SLM) as an atmospheric turbulence simulator at the Short-Wave Infra-Red (SWIR) regime and its
use with a FSLC system.
Advances in the fields of optics and optical communications have created a demand for effectively measuring
relative phase changes along an optical path or within an optical system. We present a method for obtaining these
measurements using an interferometric setup with processing involving Empirical Mode Decomposition and the
Hilbert Transform. In this work, the Hilbert Transform algorithm is justified by accurately measuring the phase
changes in software generated signals. Progress and improvements are shown regarding the ongoing design and
implementation of an experimental benchtop setup. This testbed will prove the method in applications such as
measuring and recording phase changes caused by propagating light through a turbulent freespace channel.
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