Autonomous self-configuration of artificial neural networks for data classification or system control

Artificial neural networks (ANNs) are powerful methods for the classification of multi-dimensional data as well as for the control of dynamic systems. In general terms, ANNs consist of neurons that are, e.g., arranged in layers and interconnected by real-valued or binary neural couplings or weights. ANNs try mimicking the processing taking place in biological brains. The classification and generalization capabilities of ANNs are given by the interconnection architecture and the coupling strengths. To perform a certain classification or control task with a particular ANN architecture (i.e., number of neurons, number of layers, etc.), the inter-neuron couplings and their accordant coupling strengths must be determined (1) either by a priori design (i.e., manually) or (2) using training algorithms such as error back-propagation. The more complex the classification or control task, the less obvious it is how to determine an a priori design of an ANN, and, as a consequence, the architecture choice becomes somewhat arbitrary. Furthermore, rather than being able to determine for a given architecture directly the corresponding coupling strengths necessary to perform the classification or control task, these have to be obtained/learned through training of the ANN on test data. We report on the use of a Stochastic Optimization Framework (SOF; Fink, SPIE 2008) for the autonomous self-configuration of Artificial Neural Networks (i.e., the determination of number of hidden layers, number of neurons per hidden layer, interconnections between neurons, and respective coupling strengths) for performing classification or control tasks. This may provide an approach towards cognizant and self-adapting computing architectures and systems

Automated objective characterization of visual field defects in 3D

A method and apparatus for electronically performing a visual field test for a patient. A visual field test pattern is displayed to the patient on an electronic display device and the patient's responses to the visual field test pattern are recorded. A visual field representation is generated from the patient's responses. The visual field representation is then used as an input into a variety of automated diagnostic processes. In one process, the visual field representation is used to generate a statistical description of the rapidity of change of a patient's visual field at the boundary of a visual field defect. In another process, the area of a visual field defect is calculated using the visual field representation. In another process, the visual field representation is used to generate a statistical description of the volume of a patient's visual field defect

To See or Not to See...

Vision is the primary sense used in daily life. How do we "see" the world? Do we actually "see" it or rather "perceive" it? Or is it one and the same thing? How do people with optical eye defects perceive the world? Can normally sighted people partake in their experience? If you are blind or become blind, are you blind forever? On Earth and in the spaceflight environment, there are many effects and conditions that may impair your vision or lead to irreversible vision loss or blindness, especially if undetected. The human eye and vision system can be likened to a camera consisting of an optical lens system (cornea and eye lens), film or sensor (retina), and an image-processing unit (retina and visual cortex). The malfunctioning of only one of these components will impair your vision. This paper will touch on the above topics and have (some of) the answers

Soft X--Ray Properties of Seyfert Galaxies in the Rosat All--Sky Survey

We present the results of ROSAT All-Sky Survey observations of Seyfert and IR-luminous galaxies from the Extended 12 Micron Galaxy Sample and the optically-selected CfA Sample. Roughly half of the Seyferts (mostly Seyfert 1s) have been fitted to an absorbed power-law model, yielding an average gamma of 2.26+-0.11 for 43 Seyfert 1s and 2.45+-0.18 for 10 Seyfert 2s, with both types having a median value of 2.3. The soft X-ray (SXR) luminosity correlates with the 12um luminosity, with Seyfert 1s having relatively more SXR emission than Seyfert 2s of similar mid-infrared luminosities, by a factor of 1.6+-0.3. Several physical interpretations of these results are discussed, including the standard unified model for Seyfert galaxies. Infrared-luminous non-Seyferts are shown to have similar distributions of SXR luminosity and X-ray-to-IR slope as Seyfert 2s, suggesting that some of them may harbor obscured active nuclei (as has already been shown to be true for several objects) and/or that the soft X-rays from some Seyferts 2s may be non-nuclear. A SXR luminosity function (XLF) is calculated for the 12um sample, which is well described by a single power-law with a slope of -1.75. The normalization of this XLF agrees well with that of a HXR selected sample. Several of our results, related to the XLF and the X-ray-to-IR relation are shown to be consistent with the HXR observations of the 12um sample by Barcons et al.Comment: AASTeX, 40 pages. Text and Table 2 only. PostScript versions of this file, figures, and Table 1, and a latex version of Table 1 are available by ftp://ftp.astro.ucla.edu/pub/rush/papers, get rmfv*. Accepted by ApJ ~1996 May 10. Should be published in late 199

Three-dimensional computer-automated threshold Amsler grid test

We describe a novel method for testing a visual field that employs a computer monitor with displays of varying contrast that permits unprecedented resolution and characterization of the structure of scotomas in three dimensions. Patients are placed in front of a touch-sensitive computer screen at a fixed distance. With one eye covered, they focus on a central fixation marker and trace with their finger the areas on an Amsler grid that are missing from their field of vision. Increasing degrees of contrast of the Amsler grid are simulated by repeating the test at different gray-scale levels. The results are recorded and then displayed as topographical contour rings by the computer test program. The results can also be rendered as an immediate 3-D depiction of the central hill-of-vision. Several clinical pilot studies have been conducted at the Doheny Eye Institute and more than 200 patients have been examined with this system so far. Conditions such as optic neuritis, anterior ischemic optic neuropathy (AION), age-related macular degeneration (AMD), glaucoma, and ocular hypertension have been successfully assessed by this test. Each condition provides unique patterns that are most evident in 3-D. The 3-D computer-automated threshold Amsler grid test is an innovative and noninvasive visual field test. It provides several advantages over state-of-the-art standard automated perimetry, including: (1) additional information through 3-D depiction of scotomas, such as location, extent, slope, depth, and shape; (2) high angular resolution (1 deg compared with typically 6 deg); (3) a simple test setup (merely a touch-sensitive computer monitor and the test software); (4) excellent patient compliance (spending 4 to 5 min per eye). In light of its promising initial tests, the 3-D visual field test appears to have the potential for the early detection and monitoring of various diseases over time

Method to Create Arbitrary Sidewall Geometries in 3-Dimensions Using Liga with a Stochastic Optimization Framework

Disclosed herein is a method of making a three dimensional mold comprising the steps of providing a mold substrate; exposing the substrate with an electromagnetic radiation source for a period of time sufficient to render the portion of the mold substrate susceptible to a developer to produce a modified mold substrate; and developing the modified mold with one or more developing reagents to remove the portion of the mold substrate rendered susceptible to the developer from the mold substrate, to produce the mold having a desired mold shape, wherein the electromagnetic radiation source has a fixed position, and wherein during the exposing step, the mold substrate is manipulated according to a manipulation algorithm in one or more dimensions relative to the electromagnetic radiation source; and wherein the manipulation algorithm is determined using stochastic optimization computations

Multi-rover testbed for teleconducted and autonomous surveillance, reconnaissance, and exploration

At Caltech's Visual and Autonomous Exploration Systems Research Laboratory (http://autonomy.caltech.edu) an outdoor multi-rover testbed has been developed that allows for near real-time interactive or automatic control from anywhere in the world via the Internet. It enables the implementation, field-testing, and validation of algorithms/software and strategies for navigation, exploration, feature extraction, anomaly detection, and target prioritization with applications in planetary exploration, security surveillance, reconnaissance of disaster areas, military reconnaissance, and delivery of lethal force such as explosives for urban warfare. Several rover platforms have been developed, enabling testing of cooperative multi-rover scenarios (e.g., inter-rover communication/coordination) and distributed exploration of operational areas